A key best practice to help you get the most out of the Intel^® HLS Compiler is to understanding important concepts about FPGAs. With an understanding of FPGA architecture, and some FPGA hardware design concepts and methods, you can create better designs that take advantage your target FPGA devices.

A field-programmable gate array (FPGA) is a reconfigurable semiconductor integrated circuit (IC).

FPGAs occupy a unique computational niche relative to other compute devices, such as central and graphics processing units (CPUs and GPUs), and custom accelerators, such as application-specific integrated circuits (ASICs). CPUs and GPUs have a fixed hardware structure to which a program maps, while ASICs and FPGAs can build custom hardware to implement a program.

While a custom ASIC generally outperforms an FPGA on a specific task, ASICs take significant time and money to develop. FPGAs are a cheaper off-the-shelf alternative that you can reprogram for each new application.

An FPGA is made up of a grid of configurable logic, known as adaptive logic modules (ALMs), and specialized blocks, such as digital signal processing (DSP) blocks and random-access memory (RAM) blocks. These programmable blocks are combined using configurable routing interconnects to implement complete digital circuits.

The total number of ALMs, DSP blocks, and RAM blocks used by a design is often referred to as the FPGA area or area that the design uses.

The following image illustrates a high-level architectural view of an FPGA:

The basic building block in an FPGA is an adaptive logic module (ALM).

A simplified ALM consists of a lookup table (LUT) and an output register from which the compiler can build any arbitrary Boolean logic circuit.

The following figure illustrates a simplified ALM:

A lookup table (LUT) that implements an arbitrary Boolean function of N inputs is often referred to as an N-LUT.

A register is the most basic storage element in an FPGA. It has an input (in), an output (out), and a clock signal (clk). It is synchronous, that is, it synchronizes output changes to a clock. In an ALM, a register may store the output of the LUT.

The following figure illustrates a register:

The following figure illustrates the waveform of register signals:

The input data propagates to the output on every clock cycle. The output remains unchanged between clock cycles.

A digital signal processing (DSP) block implements specific support for common fixed-point and floating-point arithmetic, which reduces the need to build equivalent logic from general-purpose ALMs.

The following figure illustrates a simplified three-input DSP block consisting of a multiplier (×) and an adder (+):

The following figure illustrates a simplified DSP block:

A random-access memory (RAM) block implements memory by using a high density of memory cells.

For more information, refer to Memory Types.

The maximum clock frequency at which a digital circuit can operate is called its f _MAX . The f_MAX is the maximum rate at which the outputs of registers are updated.

The physical propagation delay of the signal across Boolean logic between two consecutive register stages limits the clock speed. This propagation delay is a function of the complexity of the combinational logic in the path.

The path with the most combinational logic elements (and the highest delay) limits the speed of the entire circuit. This speed limiting path is often referred to as the critical path.

The f_MAX is calculated as the inverse of the critical path delay. You may want to have high f_MAX since it results in high performance in the absence of other bottlenecks.

Latency is the measure of how long it takes to complete one or more operations in a digital circuit. You can measure latency at different granularities. For example, you can measure the latency of a single operation or the latency of the entire circuit.

You can measure latency in time (for example, microseconds) or in clock cycles. Typically, clock cycles are the preferred way to express latency because measuring latency in clock cycles disconnects latency from your circuit clock frequency. By expressing latency independent of circuit clock frequency, it is easier to discern the true impact of circuit changes to the performance of the circuit.

You may want to have low latency, but lowering latency might result in decreased f_MAX.

For more information and an example, refer to Pipelining.

Pipelining is a design technique used in synchronous digital circuits to increase f_MAX. Pipelining involves adding registers to the critical path, which decreases the amount of logic between each register. Less logic takes less time to execute, which enables an increase in f_MAX.

The critical path in a circuit is the path between any two consecutive registers with the highest latency. That is, the path between two consecutive registers where the operations take the longest to complete.

Pipelining is especially useful when processing a stream of data. A pipelined circuit can have different stages of the pipeline operating on different input stream data in the same clock cycle, which leads to better data processing throughput.

Throughput of a digital circuit is the rate at which data is processed.

In the absence of other bottlenecks, higher f_MAX results in higher throughput (for example, samples/second).

Throughput is a good measure of the performance of a circuit, and throughput and performance are often used interchangeably when discussing a circuit.

A datapath is a chain of registers and Boolean logic in a digital circuit that performs computations.

For example, the datapath in Pipelining consists of all of the elements shown, from the input register to the last output register.

In contrast, memory blocks are outside the datapath and reads and writes to memory are also considered to be outside of the datapath.

While the datapath is the path on which computations occur, the control path is the path of signals that control the datapath circuitry.

The control path also consumes FPGA area, and the compiler uses techniques like clustering the datapath to help reduce the control path and save area. To learn about clustering, refer to Clustering the Datapath.

The occupancy of a datapath at a point in time refers to the proportion of the datapath that contains valid data.

The occupancy of a circuit over the execution of a program is the average occupancy over time from the moment the program starts to run until it has completed.

Unoccupied portions of the datapath are often referred to as bubbles. Bubbles are analogous to a "no operation" (no-op) instructions for a CPU that have no effect on the final output.

Decreasing bubbles increases occupancy. In the absence of other bottlenecks, maximizing occupancy of the datapath results in higher throughput.

Traditionally, you program an FPGA using a hardware description language (HDL) such as Verilog or VHDL. However, a recent trend is to use higher-level languages.

Higher levels of abstraction can reduce the design time and increase the portability of your design.

The sections that follow discuss how Intel^® HLS Compiler maps high-level languages to a hardware datapath.

Based on your source code and the following principles, the Intel^® HLS Compiler builds a custom hardware datapath in the FPGA logic:

• The datapath is functionally equivalent to the C++ program described by the source. Once the datapath is constructed, the compiler orchestrates work items and loop iterations such that the hardware is effectively occupied.
• The compiler builds the custom hardware datapath while minimizing area of the FPGA resources (like ALMs and DSPs) used by the design.

For fixed architectures, such as CPUs and GPUs, a compiler compiles code into a set of instructions that run on functional units that have a fixed functionality. For these fixed architectures to be useful in a broad range of applications, some of their available functional units are not useful to every program. Unused functional units mean that your program does not fully occupy the fixed architecture hardware.

FPGAs are not subject to these restrictions of fixed functional units. On an FPGA, you can synthesize a specialized hardware datapath that can be fully occupied for an arbitrary set of instructions, which means you can be more efficient with the silicon area of your chip.

By implementing your algorithm in hardware, you can fill your chip with custom hardware that is always (or almost always) working on your problem instead of having idle functional units.

The Intel^® HLS Compiler maps statements from the source code to individual specialized hardware operations, as shown in the example in the following image:

In general, each instruction maps to its own unique instance of a hardware operation. However, a single statement can map to more than one hardware operation, or multiple statements can combine into a single hardware operation when the compiler finds that it can generate hardware that is more efficient.

The latency of hardware operations is dependent on the complexity of the operation and the target f_MAX.

The compiler takes these hardware operations and connects them into a graph based on their dependencies. When operations are independent, the compiler automatically infers parallelism by executing those operations simultaneously in time.

The following figure shows a dependency graph created for the hardware datapath. The dependency graph shows how the instruction is mapped to hardware operations and how the hardware operations are connected based on their dependencies. The loads in this example instruction are independent of each other and can therefore run simultaneously.

Similar to the mapping of statements to specialized hardware operations, the compiler can map arrays (and structs) to hardware memories based on memory access patterns and variable sizes.

The datapath interacts with this memory through load/store units (LSUs), which are inferred from array accesses in the source code.

The following figure illustrates a simple example of mapping arrays and their accesses to hardware:

A RAM can have a limited number of read ports and write ports, but a datapath can have many LSUs. When the number of LSUs does not match the available number of read and write ports, the compiler uses techniques like replication, double pumping, sharing, and arbitration. For descriptions of these techniques, refer to Component Memory.

FPGAs provide specialized hardware block RAMs that you can configure and combine to match the size of your arrays. Customizing your memory configuration for your design can provide terabytes-per-second of on-chip memory bandwidth because each of these memories can interact with the datapath simultaneously.

Arrays might also be implemented in your component datapath. In this case, the array contents are stored as registers in the datapath when your algorithm is pipelined (as discussed in Pipelining). Storing array contents as registers in the datapath can improve performance in some cases, but it is a design decision whether to implement an array as registers or as memories.

When you access an array that is implemented as registers, LSUs are not used. The compiler might choose to use a select or a barrel shifter instead.

Scheduling refers to the process of determining the clock cycles at which each operation in the datapath executes.

Pipelining is the outcome of scheduling.

The Intel^® HLS Compiler generates pipelined datapaths that are dynamically scheduled.

A dynamically scheduled portion of the datapath does not pass data to its successor until its successor signals that it is ready to receive it.

This signaling is accomplished using handshaking control logic. For example, a variable latency load from memory may refuse to accept its predecessors' data until the load is complete.

Handshaking helps remove bubbles in the pipeline, which increases occupancy. For more information about bubbles, refer to Occupancy.

The following figure illustrates four regions of dynamically scheduled logic:

Dynamically scheduling all operations adds overhead in the form of additional FPGA area needed to implement the required handshaking control logic.

To reduce this overhead, the compiler groups fixed latency operations into clusters. A cluster of fixed latency operations, such as arithmetic operations, needs fewer handshaking interfaces, thereby reducing the area overhead.

If A, B, and C from Figure 4 do not contain variable latency operations, the compiler can cluster them together, as illustrated in Figure 5.

Clustering the logic reduces area by removing the need for signals to stall data flow in addition to other handshaking logic within the cluster.

By default, the handshaking protocol between clusters is a simple stall/valid protocol. Data from the upstream cluster is consumed when the stall signal is low and the valid signal is high.

This section describes how to map parallelism models to FPGA hardware:

Traditional instruction-set-architecture-based (ISA-based) accelerators, such as GPUs, derive data parallelism from vectorized instructions and by executing the same operation on multiple processing units.

In comparison, FPGAs derive their performance by taking advantage of their spatial architecture. FPGA compilers do not require you to vectorize your code. The compiler vectorizes your code automatically whenever it can.

As described in Mapping Source Code Instructions to Hardware, the compiler can automatically identify independent operations and execute them simultaneously in hardware.

This simulataneous execution of independent operations combined with pipelining is how performance through data parallelism is achieved on an FPGA.

The following image illustrates an example of an adder and a multiplier, which are scheduled to execute simultaneously while operating on separate inputs:

This automatic vectorization is analogous to how a superscalar processor takes advantage of instruction-level parallelism, but this vectorization happens statically at compile time instead of dynamically, at runtime.

Because determining instruction-level parallelism occurs at compile time, there is no hardware or runtime cost of dependency checking for the generated hardware datapath. Additionally, the flexible logic and routing of an FPGA means that only the available resources (like ALMs and DSPs) of the FPGA restrict the number of independent operations that can occur simultaneously.

Similar to the implementation of a CPU with multiple pipeline stages, the compiler generates a deeply-pipelined hardware datapath. For more information, refer to Concepts of FPGA Hardware Design and How Source Code Becomes a Custom Hardware Datapath.

Pipelining allows for many data items to be processed concurrently (in the same clock cycle) while making efficient use of the hardware in the datapath by keeping it occupied.

The compiler achieves concurrency by scheduling independent individual operations to execute simultaneously, but it does not achieve concurrency at coarser granularities (for example, across loops).

For larger code structures to execute in parallel with each other, you must write them as separate components or tasks that launch simultaneously. These components or tasks then run independently, and synchronize and communicate using pipes or streams, as shown in the following figure:

For details, see Systems of Tasks in the Intel^® High Level Synthesis Compiler Pro Edition Reference Manual .

If you declare an array inside your component, the Intel^® HLS Compiler creates component memory in hardware. Component memory is sometimes referred to as local memory or on-chip memory because it is created from memory resources (such as RAM blocks) available on the FPGA.

The following source code snippet results in the creation of a component memory system, an interface to an external memory system, and access to these memory systems:

#include <HLS/hls.h>

constexpr int SIZE = 128;
constexpr int N = SIZE - 1;

using MasterInterface = ihc::mm_master<int, ihc::waitrequest<true>, 
                                       ihc::latency<0>>;

component void memoryComponent(MasterInterface &masterA)
{
    hls_memory int T[SIZE]; // declaring an array as a component memory
    for (unsigned i = 0; i < SIZE; i++)
    {
        T[i] = i; // writing to component memory
    }
    for (int i = 0; i < N; i += 2)
    {
        // reading from a component memory and writing to a external 
        // Avalon memory-mapped slave component through an Avalon
        // memory-mapped master interface
        masterA[i] = T[i] + T[i + 1];
    }
}

The compiler performs the following tasks to build a memory system:

• Build a component memory from FPGA memory resources (such as block RAMs) and presents it to the datapath as a single memory.
• Map each array access to a load-store unit (LSU) in the datapath that transacts with the component memory through its ports.
• Automatically optimizes the component memory geometry to maximize the bandwidth available to loads and stores in the datapath.
• Attempts to guarantee that component memory accesses never stall.

If the component accesses memory outside of the component, the compiler creates a hardware interface through which the datapath accesses this external memory. The interface is described using a pointer or Avalon^® memory-mapped master interface as a function argument to the component. One interface is created for every pointer or memory-mapped master interface component argument.

The code snippet in Component Memory shows an external memory described with an Avalon^® memory-mapped master interface and its accesses within the component.

Unlike component memory, the compiler does not define the structure of the external memory. The compiler instantiates a specialized LSU for each access site based on the type of interface and the memory access patterns.

The compiler also tries various strategies to maximize the efficient use of the available memory interface bandwidth such as eliminating unnecessary accesses and statically coalescing contiguous accesses.

With the Intel^® High Level Synthesis Compiler, your component can have a variety of interfaces: from basic wires to the Avalon Streaming and Avalon Memory-Mapped Master interfaces. Review the interface best practices to help you choose and configure the right interface for your component.

Each interface type supported by the Intel^® HLS Compiler Pro Edition has different benefits. However, the system that surrounds your component might limit your choices. Keep your requirements in mind when determining the optimal interface for your component.

<quartus_installdir>/hls/examples/tutorials

Controlling the type of load-store units (LSUs) that the Intel^® HLS Compiler Pro Edition uses to interact with variable-latency Memory Mapped (MM) Master interfaces can help save area in your design. You might also encounter situations where disabling static coalescing of a load/store with other load/store operations benefits the performance of your design.

Review the following tutorial to learn about controlling LSUs: <quartus_installdir>/hls/examples/tutorials/best_practices/lsu_control.

To see if you need to use LSU controls, review the High-Level Design Reports for your component, especially the Function Memory Viewer, to see if the memory access pattern (and its associated LSUs) inferred by the Intel^® HLS Compiler Pro Edition match your expected memory access pattern. If they do not match, consider controlling the LSU type, LSU coalescing, or both.

component void
dut(mm_master<int, dwidth<128>, awidth<32>, aspace<4>, latency<0>> &Buff1,
    mm_master<int, dwidth<32>, awidth<32>, aspace<5>, latency<0>> &Buff2) {
  int Temp[SIZE];

  using pipelined = lsu<style<PIPELINED>>; 
  using burst_coalesced = lsu<style<BURST_COALESCED>>;

  for (int i = 0; i<SIZE; i++) {
    Temp[i] = burst_coalesced::load(&Buff1[i]); // Burst-Coalesced LSU
  }

  for (int i = 0; i<SIZE; i++) {
    pipelined::store(&Buff2[i], 2*Temp[i]); // Pipelined LSU
  }
}

component int
dut(mm_master<int, dwidth<256>, awidth<32>, aspace<1>, latency<0>> &Buff1,
    int i, bool Cond1, bool Cond2) {

  using no_coalescing = lsu<style<PIPELINED>, static_coalescing<false>>;
  int Val = 0;
  if (Cond1) {
    Val = no_coalescing::load(&Buff1[i]);
  }
  if (Cond2) {
    Val = no_coalescing::load(&Buff1[i + 1]);
  }
  return Val;
}

Add a restrict type-qualifier to pointer types whenever possible. By having restrict-qualified pointers, you prevent the Intel^® HLS Compiler Pro Edition from creating unnecessary memory dependencies between nonconflicting read and write operations.

The restrict type-qualifier is __restrict.

Consider a loop where each iteration reads data from one array, and then it writes data to another array in the same physical memory. Without adding the restrict type-qualifier to these pointer arguments, the compiler must assume that the two arrays might overlap. Therefore, the compiler must keep the original order of memory accesses to both arrays, resulting in poor loop optimization or even failure to pipeline the loop that contains the memory accesses.

You can also use the restrict type-qualifier with Avalon^® memory-mapped (MM) master interfaces.

<quartus_installdir>/hls/examples/tutorials/best_practices/parameter_aliasing

The Intel^® HLS Compiler Pro Edition lets you know if there are any dependencies that prevent it from optimizing your loops. Try to eliminate these dependencies in your code for optimal component performance. You can also provide additional guidance to the compiler by using the available loop pragmas.

<quartus_installdir>/hls/examples/tutorials

Loops are a useful way to reuse hardware. If your component function calls another function, the called function will be the top-level component. Calling a function multiple times results in hardware duplication.

int foo(int a)
{
    return 4 + sqrt(a) /
}

component
void myComponent()
{
  ...
  int x =
  x += foo(0);
  x += foo(1);
  x += foo(2);
  ...
}

component
void myComponent()
{
  ...
  int x = 0;
#pragma unroll 1
  for (int i = 0; i < 3; i++)
  {
    x += foo(i);
  }
  ...
}

component
void myComponent()
{
  ...
  int x = 0;
#pragma unroll 1
  for (int i = 0; i < 3; i++)
  {
     int val = 0;
     switch(i)
     {
     case 0:
         val = 3;
         break;
     case 1:
         val = 6;
         break;
     case 2:
         val = 1;
         break;
     }
     x += foo(val);
  }
  ...
}
H

You can learn more about reusing hardware and minimizing inlining be reviewing the resource sharing tutorial available in <quartus_installdir>/hls/examples/tutorials/best_practices/resource_sharing_filter.

You can take advantage of the spatial compute structure to accelerate the loops by having multiple iterations of a loop executing concurrently. To have multiple iterations of a loop execute concurrently, unroll loops when possible and structure your loops so that dependencies between loop iterations are minimized and can be resolved within one clock cycle.

These practices show how to parallelize different iterations of the same loop. If you have two different loops that you want to parallelize, consider using a system of tasks. For details, see System of Tasks Best Practices.

Consider the following basic loop with three stages and three iterations. A loop stage is defined as the operations that occur in the loop within one clock cycle.

Figure 29. Basic loop with three stages and three iterations

If each stage of this loop takes one clock cycle to execute, then this loop has a latency of nine cycles.

The following figure shows the pipelining of the loop from Figure 29.

Figure 30. Pipelined loop with three stages and four iterations

The pipelined loop has a latency of five clock cycles for three iterations (and six cycles for four iterations), but there is no area tradeoff. During the second clock cycle, Stage 1 of the pipeline loop is processing iteration 2, Stage 2 is processing iteration 1, and Stage 3 is inactive.

This loop is pipelined with a loop initiation interval (II) of 1. An II of 1 means that there is a delay of 1 clock cycle between starting each successive loop iteration.

The Intel^® HLS Compiler Pro Edition attempts to pipeline loops by default, and loop pipelining is not subject to the same constant iteration count constraint that loop unrolling is.

Not all loops can be pipelined as well as the loop shown in Figure 30, particularly loops where each iteration depends on a value computed in a previous iteration.

For example, consider if Stage 1 of the loop depended on a value computed during Stage 3 of the previous loop iteration. In that case, the second (orange) iteration could not start executing until the first (blue) iteration had reached Stage 3. This type of dependency is called a loop-carried dependency.

In this example, the loop would be pipelined with II=3. Because the II is the same as the latency of a loop iteration, the loop would not actually be pipelined at all. You can estimate the overall latency of a loop with the following equation:

${\mathrm{latency}}_{\mathrm{loop}}=(\mathrm{iterations}-1)*\mathrm{II}+{\mathrm{latency}}_{\mathrm{body}}$

where ${\mathrm{latency}}_{\mathrm{loop}}$ is the number of cycles the loop takes to execute and ${\mathrm{latency}}_{\mathrm{body}}$ is the number of cycles a single loop iteration takes to execute.

The Intel^® HLS Compiler Pro Edition supports pipelining nested loops without unrolling inner loops. When calculating the latency of nested loops, apply this formula recursively. This recursion means that having II>1 is more problematic for inner loops than for outer loops. Therefore, algorithms that do most of their work on an inner loop with II=1 still perform well, even if their outer loops have II>1.

Consider the following basic loop with three stages and three iterations. Each stage represents the operations that occur in the loop within one clock cycle.

Figure 31. Basic loop with three stages and three iterations

If each stage of this loop takes one clock cycle to execute, then this loop has a latency of nine cycles.

The following figure shows the loop from Figure 31 unrolled three times.

Figure 32. Unrolled loop with three stages and three iterations

Three iterations of the loop can now be completed in only three clock cycles, but three times as many hardware resources are required.

You can control how the compiler unrolls a loop with the #pragma unroll directive, but this directive works only if the compiler knows the trip count for the loop in advance or if you specify the unroll factor. In addition to replicating the hardware, the compiler also reschedules the circuit such that each operation runs as soon as the inputs for the operation are ready.

For an example of using the #pragma unroll directive, see the best_practices/resource_sharing_filter tutorial.

1.	#define ROWS 4
2.	#define COLS 4
3.	
4.	component void dut(...) {
5.		float a_matrix[COLS][ROWS]; // store in column-major format
6.		float r_matrix[ROWS][COLS]; // store in row-major format
7.	
8.		// setup...
9.	
10.		for (int i = 0; i < COLS; i++) {
11.			for (int j = i + 1; j < COLS; j++) {
12.	
13.				float dotProduct = 0;
14.				for (int mRow = 0; mRow < ROWS; mRow++) {
15.					dotProduct += a_matrix[i][mRow] * a_matrix[j][mRow];
16.				}
17.				r_matrix[i][j] = dotProduct;
18.			}
19.		}
20.	 	
21.	 // continue...
22.		
23.	}

You can improve the performance of this component by unrolling the loops that iterate across each entry of a particular column. If the loop operations are independent, then the compiler executes them in parallel.

Floating-point operations typically must be carried out in the same order that they are expressed in your source code to preserve numerical precision. However, you can use the -ffp-contract=fast compiler flag to relax the ordering of floating-point operations. With the order of floating-point operations relaxed, all of the multiplications in this loop can occur in parallel. To learn more, review the tutorial: <quartus_installdir>/hls/examples/ tutorials/best_practices/ floating_point_ops

The compiler tries to unroll loops on its own when it thinks unrolling improves performance. For example, the loop at line 14 is automatically unrolled because the loop has a constant number of iterations, and does not consume much hardware (ROWS is a constant defined at compile-time, ensuring that this loop has a fixed number of iterations).

01: #define ROWS 4
02: #define COLS 4
03: 
04: component void dut(...) {
05: 	float a_matrix[COLS][ROWS]; // store in column-major format
06: 	float r_matrix[ROWS][COLS]; // store in row-major format
07: 
08: 	// setup...
09: 
10: 	for (int i = 0; i < COLS; i++) {
11: 
12: #pragma unroll
13: 			for (int j = 0; j < COLS; j++) {
14: 				float dotProduct = 0;
15: 
16: #pragma unroll

17: 				for (int mRow = 0; mRow < ROWS; mRow++) {
18: 					dotProduct += a_matrix[i][mRow] * a_matrix[j][mRow];
19: 				}
20: 
21: 				r_matrix[i][j] = (j > i) ? dotProduct : 0; // predication
22: 			}
23: 		}
24:  	}
25:  	
26: 	// continue...
27: 	
28: }

Now the j-loop is fully unrolled. Because they do not have any dependencies, all four iterations run at the same time.

Refer to the resource_sharing_filter tutorial located at <quartus_installdir>/hls/examples/tutorials/best_practices/resource_sharing_filter for more details.

You could continue and also unroll the loop at line 10, but unrolling this loop would result in the area increasing again. By allowing the compiler to pipeline this loop instead of unrolling it, you can avoid increasing the area and pay about only four more clock cycles assuming that the i-loop only has an II of 1. If the II is not 1, the Details pane of the Loops Analysis page in the high-level design report (report.html) gives you tips on how to improve it.

A well-formed loop has an exit condition that compares against an integer bound and has a simple induction increment of one per iteration. The Intel^® HLS Compiler Pro Edition can analyze well-formed loops efficiently, which can help improve the performance of your component.

for(int i=0; i < N; i++)
{
    //statements
}

Well-formed nested loops can also help maximize the performance of your component.

for(int i=0; i < N; i++)
{
   //statements
   for(int j=0; j < M; j++)
   {
      //statements
   }
}

If a loop in your component has complex exit conditions, memory accesses or complex operations might be required to evaluate the condition. Subsequent iterations of the loop cannot launch in the loop pipeline until the evaluation completes, which can decrease the overall performance of the loop.

Use the speculated_iterations pragma to specify how many cycles the loop exit condition can take to compute.

To maximize performance, combine nested loops into a single loop whenever possible. The control flow for a loop adds overhead both in logic required and FPGA hardware footprint. Combining nested loops into a single loop reduces these aspects, improving the performance of your component.

The following code examples illustrate the conversion of a nested loop into a single loop:

for (i = 0; i < N; i++)
{
    //statements
    for (j = 0; j < M; j++)
    {
        //statements          
    }
    //statements
} 

for (i = 0; i < N*M; i++)
{
    //statements
}

You can also specify the loop_coalesce pragma to coalesce nested loops into a single loop without affecting the loop functionality. The following simple example shows how the compiler coalesces two loops into a single loop when you specify the loop_coalesce pragma.

#pragma loop_coalesce
for (int i = 0; i < N; i++)
 for (int j = 0; j < M; j++)
  sum[i][j] += i+j;

int i = 0;
int j = 0;
while(i < N){

  sum[i][j] += i+j;
  j++;
  
  if (j == M){
    j = 0;
    i++;
  }
}

For more information about the loop_coalesce pragma, see "Loop Coalescing (loop_coalesce Pragma)" in Intel^® High Level Synthesis Compiler Pro Edition Reference Manual.

You can also review the following tutorial: <quartus_installdir>/hls/examples/tutorials/best_practices/loop_coalesce

If you have a nests of loops, avoid placing loops within conditional statements.

These conditions can cause the outer loop to take different paths (divergent loops), which can reduce the QoR of your component because these condition prevent the Intel^® HLS Compiler from pipelining the loops.

for (int row = 0; row < outerTripCount; row++) {
  if (loopCondition)  {
    for (int col = 0; col < innerTripCount; col++) {
      foo();
      }
    }
  else {
    for (int col = 0; col < innerTripCount; col++) {
      bar();
    }
  }
}

for (int row = 0; row < outerTripCount; row++) {
  for (int col = 0; col < innerTripCount; col++) {
    if (loopCondition) {
      foo();
      }
    else {
      bar();
      }
   }
}

<quartus_installdir>/hls/examples/tutorials/best_practices/divergent_loops

To reduce the FPGA hardware resources necessary for implementing a variable, declare the variable just before you use it in a loop. Declaring variables in the deepest scope possible minimizes data dependencies and FPGA hardware usage because the Intel^® HLS Compiler Pro Edition does not need to preserve the variable data across loops that do not use the variables.

Consider the following example:

int a[N];
for (int i = 0; i < m; ++i)
{
    int b[N];
    for (int j = 0; j < n; ++j)
	{
        // statements
    }
}

The array a requires more resources to implement than the array b. To reduce hardware usage, declare array a outside the inner loop unless it is necessary to maintain the data through iterations of the outer loop.

If you have a loop that does not affect the throughput of your component, you can raise the initiation interval (II) of the loop with the ii pragma to try and increase the f_MAX of your design.

The initiation interval (II) of a loop is the statically determined number of cycles between successive iteration launches of a given loop invocation. However, the statically scheduled II may differ from the realized dynamic II when considering interleaving.

With loop interleaving, the dynamic II of a loop can be approximated by the static II of the loop divided by the degree of interleaving, that is, by the number of concurrent invocations of the loop that are in flight.

Interleaving allows the iterations of more than one invocation of a loop to execute in parallel, provided that the static II of that loop is greater than 1. By default, the maximum amount of interleaving for a loop is equal to the static II of that loop.

In the presence of interleaving, the dynamic II of a loop can be approximated by the static II of the loop divided by the degree of interleaving, that is, by the number of concurrent invocations of the loop that are in flight.

Review the following tutorial to learn more about loop interleaving and how to control it: <quartus_installdir>/hls/examples/tutorials/loop_controls/ max_interleaving.

The compiler attempts to optimize the component for different objectives for the scheduled f_MAX depending on whether the f_MAX target is set and whether the #pragma II is set for each of the loops.

The f_MAX target is a strong suggestion and the compiler does not error out if it is not able to achieve this f_MAX, whereas the #pragma II triggers an error if the compiler cannot achieve the requested II. The f_MAX achieved for each block of code is shown in the Loops report.

The following table outlines the behavior of the scheduler in the Intel^® HLS Compiler:

In most cases, you can optimize the memory architecture by modifying the access pattern. However, the Intel^® HLS Compiler Pro Edition gives you some control over the memory architecture.

<quartus_installdir>/hls/examples/tutorials/component_memories

Using memory attributes in various combinations in your code allows you to override the memory architecture that the Intel^® HLS Compiler Pro Edition infers for your component.

The following code examples demonstrate how you can use the following memory attributes to override coalesced memory to conserve memory blocks on your FPGA:

• hls_bankwidth(N)
• hls_numbanks(N)
• hls_singlepump
• hls_max_replicates(N)

The original code coalesces two memory accesses, resulting in a memory system that is 256 locations deep by 64 bits wide (256x64 bits) (two on-chip memory blocks):

component unsigned int mem_coalesce_default(unsigned int raddr,
                                            unsigned int waddr,
                                            unsigned int wdata){
    unsigned int data[512];
    data[2*waddr] = wdata;
    data[2*waddr + 1] = wdata + 1;
    unsigned int rdata = data[2*raddr] + data[2*raddr + 1];
    return rdata;
}

The following images show how the 256x64 bit memory for this code sample is structured, as well how the component memory structure is shown in the high-level design report (report.html)

Figure 33. Memory Structure Generated for mem_coalesce_default

The modified code implements a single on-chip memory block that is 512 words deep by 32 bits wide with stallable arbitration:

component unsigned int mem_coalesce_override(unsigned int raddr,
                                             unsigned int waddr,
                                             unsigned int wdata){
    //Attributes that stop memory coalescing
    hls_bankwidth(4) hls_numbanks(1)
    //Attributes that specify a single-pumped single-replicate memory
    hls_singlepump hls_max_replicates(1)
    unsigned int data[512];
    data[2*waddr] = wdata;
    data[2*waddr + 1] = wdata + 1;
    unsigned int rdata = data[2*raddr] + data[2*raddr + 1];
    return rdata;
} 

The following images show how the 512x32 bit memory with stallable arbitration for this code sample is structured, as well how the component memory structure is shown in the high-level design report (report.html).

Figure 34. Memory Structure Generated for mem_coalesce_override

While it might appear that you save hardware area by reducing the number of RAM blocks needed for the component, the introduction of stallable arbitration increases the amount of hardware needed to implement the component. In the following table, you can compare the number ALMs and FFs required by the components.

Using memory attributes in various combinations in your code allows you to override the memory architecture that the Intel^® HLS Compiler Pro Edition infers for your component.

The following code examples demonstrate how you can use the following memory attributes to override banked memory to conserve memory blocks on your FPGA:

• hls_bankwidth(N)
• hls_numbanks(N)
• hls_singlepump
• hls_doublepump

The original code creates two banks of single-pumped on-chip memory blocks that are 16 bits wide:

component unsigned short mem_banked(unsigned short raddr,
                                    unsigned short waddr,
                                    unsigned short wdata){
    unsigned short data[1024];
    
    data[2*waddr] = wdata;
    data[2*waddr + 9] = wdata +1;

    unsigned short rdata = data[2*raddr] + data[2*raddr + 9];

    return rdata;
}

To save banked memory, you can implement one bank of double-pumped 32-bit-wide on-chip memory block by adding the following attributes before the declaration of data[1024]. These attributes fold the two half-used memory banks into one fully-used memory bank that is double pumped, so that it can be accessed as quickly as the two half-used memory banks.

hls_bankwidth(2) hls_numbanks(1)
hls_doublepump
unsigned short data[1024];

Alternatively, you can avoid the double-clock requirement of the double-pumped memory by implementing one bank of single-pumped on-chip memory block by adding the following attributes before the declaration of data[1024]. However, in this example, these attributes add stallable arbitration to your component memories, which hurts your component performance.

hls_bankwidth(2) hls_numbanks(1)
hls_singlepump
unsigned short data[1024];

In some cases, you can save FPGA memory blocks by merging your component memories so that they consume fewer memory blocks, reducing the FPGA area your component uses. Use the hls_merge attribute to force the Intel^® HLS Compiler Pro Edition to implement different variables in the same memory system.

When you merge memories, multiple component variables share the same memory block. You can merge memories by width (width-wise merge) or depth (depth-wise merge). You can merge memories where the data in the memories have different datatypes.

Use the hls_merge("<mem_name>","depth") attribute to force the Intel^® HLS Compiler Pro Edition to implement variables in the same memory system, merging their memories by depth.

All variables with the same <mem_name> label set in their hls_merge attributes are merged.

Consider the following component code:

component int depth_manual(bool use_a, int raddr, int waddr, int wdata) {
  int a[128];
  int b[128];

  int rdata;

  // mutually exclusive write
  if (use_a) {
    a[waddr] = wdata;
  } else {
    b[waddr] = wdata;
  }

  // mutually exclusive read
  if (use_a) {
    rdata = a[raddr];
  } else {
    rdata = b[raddr];
  }

  return rdata;
}

The code instructs the Intel^® HLS Compiler Pro Edition to implement local memories a and b as two on-chip memory blocks, each with its own load and store instructions.

Because the load and store instructions for local memories a and b are mutually exclusive, you can merge the accesses, as shown in the example code below. Merging the memory accesses reduces the number of load and store instructions, and the number of on-chip memory blocks, by half.

component int depth_manual(bool use_a, int raddr, int waddr, int wdata) {
  int a[128] hls_merge("mem","depth");
  int b[128] hls_merge("mem","depth");

  int rdata;

  // mutually exclusive write
  if (use_a) {
    a[waddr] = wdata;
  } else {
    b[waddr] = wdata;
  }

  // mutually exclusive read
  if (use_a) {
    rdata = a[raddr];
  } else {
    rdata = b[raddr];
  }

  return rdata;
}

There are cases where merging local memories with respect to depth might degrade memory access efficiency. Before you decide whether to merge the local memories with respect to depth, refer to the HLD report ( <result>.prj/reports/report.html) to ensure that they have produced the expected memory configuration with the expected number of loads and stores instructions. In the example below, the Intel^® HLS Compiler Pro Edition should not merge the accesses to local memories a and b because the load and store instructions to each memory are not mutually exclusive.

component int depth_manual(bool use_a, int raddr, int waddr, int wdata) {
  int a[128] hls_merge("mem","depth");
  int b[128] hls_merge("mem","depth");

  int rdata;

  // NOT mutually exclusive write

  a[waddr] = wdata;
  b[waddr] = wdata;
  
  // NOT mutually exclusive read

  rdata = a[raddr];
  rdata += b[raddr];
  
  return rdata;
}

In this case, the Intel^® HLS Compiler Pro Edition might double pump the memory system to provide enough ports for all the accesses. Otherwise, the accesses must share ports, which prevent stall-free accesses.

Use the hls_merge("<mem_name>","width") attribute to force the Intel^® HLS Compiler Pro Edition to implement variables in the same memory system, merging their memories by width.

All variables with the same <mem_name> label set in their hls_merge attributes are merged.

Consider the following component code:

component short width_manual (int raddr, int waddr, short wdata) {

  short a[256];
  short b[256];

  short rdata = 0;
  
  // Lock step write
  a[waddr] = wdata;
  b[waddr] = wdata;

  // Lock step read 
  rdata += a[raddr];
  rdata += b[raddr];

  return rdata;
}

In this case, the Intel^® HLS Compiler Pro Edition can coalesce the load and store instructions to local memories a and b because their accesses are to the same address, as shown below.

component short width_manual (int raddr, int waddr, short wdata) {

  short a[256] hls_merge("mem","width");
  short b[256] hls_merge("mem","width");

  short rdata = 0;
  
  // Lock step write
  a[waddr] = wdata;
  b[waddr] = wdata;

  // Lock step read 
  rdata += a[raddr];
  rdata += b[raddr];

  return rdata;
}

The (b ₀ , b ₁ , ... ,b _n ) arguments refer to the local memory address bit positions that the Intel^® HLS Compiler Pro Edition should use for the bank-selection bits. Specifying the hls_bankbits(b ₀, b ₁ , ..., b _n) attribute implies that the number of banks equals 2 ^{number of bank bits} .

Example of Implementing the hls_bankbits Attribute

Consider the following example component code:

¹

component int bank_arbitration (int raddr,
                                int waddr,
                                int wdata) {

  #define DIM_SIZE 4

  // Adjust memory geometry by preventing coalescing
  hls_numbanks(1)
  hls_bankwidth(sizeof(int)*DIM_SIZE)

  // Force each memory bank to have 2 ports for read/write
  hls_singlepump
  hls_max_replicates(1) 

  int a[DIM_SIZE][DIM_SIZE][DIM_SIZE];
  // initialize array a…
  int result = 0; 
 
  #pragma unroll
  for (int dim1 = 0; dim1 < DIM_SIZE; dim1++)
    #pragma unroll
    for (int dim3 = 0; dim3 < DIM_SIZE; dim3++)
      a[dim1][waddr&(DIM_SIZE-1)][dim3] = wdata;

  #pragma unroll
  for (int dim1 = 0; dim1 < DIM_SIZE; dim1++)
    #pragma unroll
    for (int dim3 = 0; dim3 < DIM_SIZE; dim3++)
      result += a[dim1][raddr&(DIM_SIZE-1)][dim3];

  return result;
}

As illustrated in the following figure, this code example generates multiple load and store instructions, and therefore multiple load/store units (LSUs) in the hardware. If the memory system is not split into multiple banks, there are fewer ports than memory access instructions, leading to arbitrated accesses. This arbitration results in a high loop initiation interval (II) value. Avoid arbitration whenever possible because it increases the FPGA area utilization of your component and impairs the performance of your component.

Figure 41. Accesses to Local Memory a for Component bank_arbitration

By default, the Intel^® HLS Compiler Pro Edition splits the memory into banks if it determines that the split is beneficial to the performance of your component. The compiler checks if any bits remain constant between accesses, and automatically infers bank-selection bits.

Now, consider the following component code:

component int bank_no_arbitration (int raddr, 
                                   int waddr, 
                                   int wdata) {
  #define DIM_SIZE 4

  // Adjust memory geometry by preventing coalescing and splitting memory
  hls_bankbits(4, 5)
  hls_bankwidth(sizeof(int)*DIM_SIZE)

  // Force each memory bank to have 2 ports for read/write
  hls_singlepump
  hls_max_replicates(1)
 
  int a[DIM_SIZE][DIM_SIZE][DIM_SIZE];

  // initialize array a…
  int result = 0; 
 
  #pragma unroll
  for (int dim1 = 0; dim1 < DIM_SIZE; dim1++)
    #pragma unroll
    for (int dim3 = 0; dim3 < DIM_SIZE; dim3++)
      a[dim1][waddr&(DIM_SIZE-1)][dim3] = wdata;

  #pragma unroll
  for (int dim1 = 0; dim1 < DIM_SIZE; dim1++)
    #pragma unroll
    for (int dim3 = 0; dim3 < DIM_SIZE; dim3++)
      result += a[dim1][raddr&(DIM_SIZE-1)][dim3];

  return result;
}

The following diagram shows that this example code creates a memory configuration with four banks. Using bits 4 and 5 as bank selection bits ensures that each load/store access is directed to its own memory bank.

Figure 42. Accesses to Local Memory a for Component bank_no_arbitration

In this code example, setting hls_numbanks(4) instead of hls_bankbits(4,5) results in the same memory configuration because the Intel^® HLS Compiler Pro Edition automatically infers the optimal bank selection bits.

In the Function Memory Viewer (inf the High-Level Design Reports), the Address bit information shows the bank selection bits as b6 and b7, instead of b4 and b5:

This difference occurs because the address bits reported in the Function Memory Viewer are based on byte addresses and not element addresses. Because every element in array a is four bytes in size, bits b4 and b5 in element address bits correspond to bits b6 and b7 in byte addressing.

After you implement systems of tasks, you might want to balance the capacity of your task functions. For details, review the advice in Balancing Capacity in a System of Tasks.

component void foo() {
  // first loop
  for (int i = 0; i < n; i++) {
    // Do something
  }
  // second loop
  for (int i = 0; i < m; i++) {
    // Do something else
  }
}

void first_loop() {
  for (int i = 0; i < n; i++) {
    // Do something
  }
}

void second_loop() {
  for (int i = 0; i < m; i++) {
    // Do something else
  }
}

component void foo() {
  ihc::launch<first_loop>();
  ihc::launch<second_loop>();
  ihc::collect<first_loop>();
  ihc::collect<second_loop>();
}

Review the tutorial <quartus_installdir>/hls/examples/tutorials/system_of_tasks/parallel_loop to learn more about how to run multiple loops in parallel.

To allow for calls from multiple places to a task, the Intel^® HLS Compiler Pro Edition generates arbitration logic to the called task function. This arbitration logic can increase the area utilization of the component. However, if the shared logic is large, the trade-off can help you save FPGA resources. The savings can be especially noticed when your component has a large compute block that is not always active.

Review the tutorial <quartus_installdir>/hls/examples/tutorials/system_of_tasks/resource_sharing to see a simple example of how to share a compute block in component.

If you do not use a system of tasks, function calls in your HLS component are in-lined and optimized together with the calling code, which can be detrimental in some situations. Use a system of tasks to prevent smaller blocks of your design from being affected by the rest of the system.

Typically, these performance issues are caused by a lack of capacity in the datapath of the functions calling task function using the ihc::launch and ihc::collect calls. You can improve system throughput in these cases by adding a buffer to the explicit streams to account for the latency of the task functions.

The Intel^® HLS Compiler Pro Edition emulator models the size of the buffer attached to a stream. However, the emulator does not fully account for hardware latencies, and it might exhibit different behavior between simulation and emulation in these cases.

In addition to the techniques outlined in the tutorials, follow the practices that follow to try to maximize the data throughput of your design.

In many situations, the Intel^® HLS Compiler can add buffer capacity automatically to the data path in a system of tasks design to achieve maximum throughput for your design. Follow a few best practices to help the Intel^® HLS Compiler effectively add data path buffer capacity to your design when needed.

component foo() {
  // Parse/compute data for tasks
  ihc::launch<task1>(data1);
  ihc::launch<task2>(data2);

  auto r1 = ihc::collect<task1>();
  auto r2 = ihc::collect<task2>();
  // Usage of r1, r2
}

In this diagram, Entry represents the two independent launch calls, and the Exit represents the two independent collect calls.

Entry provides work to both tasks only if both tasks can take in data (that is, both task have available buffer capacity). Similarly, Exit consumes the results only when both results are available.

If Task1 and Task2 have the same number of pipeline stages, then the data path performs at full throughput. Some data path buffer capacity is needed in the caller function to ensure that the caller can continue issuing launch calls while the collect calls wait for the task functions to complete. The compiler adds this data path buffer capacity automatically.

If the two tasks have different pipeline depths, then the design encounters a bottleneck because the task with the smaller pipeline depth lacks the buffer capacity to store finished results while waiting for the other task to finish. In this case, you can add buffer capacity to either launch or the collect call of the task with the smaller pipeline depth. For details about adding launch/collect buffer capacity, see Explicitly Add Buffer Capacity to Your Design When Needed.

The Intel^® HLS Compiler tries to balance data path buffer capacity automatically, but it can only add data path capacity automatically when your design follows certain practices.

When the Intel^® HLS Compiler cannot infer the optimal capacity requirements, you can explicitly add buffer capacity to your design by specifying a value for the capacity parameter of the ihc::launch and ihc::collect functions.

After you optimize the algorithm bottlenecks of your design, you can fine-tune some datatypes in your component by using arbitrary precision datatypes to shrink data widths, which reduces FPGA area utilization. The Intel^® HLS Compiler Pro Edition provides debug functionality so that you can easily detect overflows in arbitrary precision datatypes.

Because C++ automatically promotes smaller datatypes such as short or char to 32 bits for operations such as addition or bit-shifting, you must use the arbitrary precision datatypes if you want to create narrow datapaths in your component.

<quartus_installdir>/hls/examples/tutorials

The ac_int datatype differs from other languages, including C and Verilog, in bit shifting. By default, if the shift amount is of a signed datatype ac_int allows negative shifts.

In hardware, this negative shift results in the implementation of both a left shifter and a right shifter. The following code example shows a shift amount that is a signed datatype.

int14 shift_left(int14 a, int14 b) {
    return (a << b);
} 

If you know that the shift is always in one direction, to implement an efficient shift operator, declare the shift amount as an unsigned datatype as follows:

int14 efficient_left_only_shift(int14 a, uint14 b) {
    return (a << b);
}