Intel® High Level Synthesis Compiler Pro Edition: Best Practices Guide

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ID 683152
Date 12/19/2022
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Document Table of Contents
Document Table of Contents x
1. Intel® HLS Compiler Pro Edition Best Practices Guide 2. Best Practices for Coding and Compiling Your Component 3. FPGA Concepts 4. Interface Best Practices 5. Loop Best Practices 6. fMAX Bottleneck Best Practices 7. Memory Architecture Best Practices 8. System of Tasks Best Practices 9. Datatype Best Practices 10. Advanced Troubleshooting A. Intel® HLS Compiler Pro Edition Best Practices Guide Archives B. Document Revision History for Intel® HLS Compiler Pro Edition Best Practices Guide
1. Intel® HLS Compiler Pro Edition Best Practices Guide x
1.1. Pending Deprecation of the Intel® HLS Compiler
3. FPGA Concepts x
3.1. FPGA Architecture Overview 3.2. Concepts of FPGA Hardware Design 3.3. Methods of Hardware Design
3.1. FPGA Architecture Overview x
3.1.1. Adaptive Logic Module (ALM) 3.1.2. Digital Signal Processing (DSP) Block 3.1.3. Random-Access Memory (RAM) Blocks
3.1.1. Adaptive Logic Module (ALM) x
3.1.1.1. Lookup Table (LUT) 3.1.1.2. Register
3.2. Concepts of FPGA Hardware Design x
3.2.1. Maximum Frequency (fMAX) 3.2.2. Latency 3.2.3. Pipelining 3.2.4. Throughput 3.2.5. Datapath 3.2.6. Control Path 3.2.7. Occupancy
3.3. Methods of Hardware Design x
3.3.1. How Source Code Becomes a Custom Hardware Datapath 3.3.2. Scheduling 3.3.3. Mapping Parallelism Models to FPGA Hardware 3.3.4. Memory Types
3.3.1. How Source Code Becomes a Custom Hardware Datapath x
3.3.1.1. Mapping Source Code Instructions to Hardware 3.3.1.2. Mapping Arrays and Their Accesses to Hardware
3.3.2. Scheduling x
3.3.2.1. Dynamic Scheduling 3.3.2.2. Clustering the Datapath 3.3.2.3. Handshaking Between Clusters
3.3.3. Mapping Parallelism Models to FPGA Hardware x
3.3.3.1. Data Parallelism 3.3.3.2. Task Parallelism
3.3.3.1. Data Parallelism x
3.3.3.1.1. Executing Independent Operations Simultaneously 3.3.3.1.2. Pipelining
3.3.3.1.2. Pipelining x
3.3.3.1.2.1. Pipelining Loops Within A Component 3.3.3.1.2.2. Pipelining Across Component Invocations
3.3.4. Memory Types x
3.3.4.1. Component Memory 3.3.4.2. External Memory
4. Interface Best Practices x
4.1. Choose the Right Interface for Your Component 4.2. Control LSUs For Your Variable-Latency MM Host Interfaces 4.3. Avoid Pointer Aliasing
4.1. Choose the Right Interface for Your Component x
4.1.1. Pointer Interfaces 4.1.2. Avalon® Memory Mapped Host Interfaces 4.1.3. Avalon® Memory Mapped Agent Memories 4.1.4. Avalon® Memory Mapped Agent Registers 4.1.5. Avalon® Streaming Interfaces 4.1.6. Pass-by-Value Interface
5. Loop Best Practices x
5.1. Reuse Hardware By Calling It In a Loop 5.2. Parallelize Loops 5.3. Construct Well-Formed Loops 5.4. Minimize Loop-Carried Dependencies 5.5. Avoid Complex Loop-Exit Conditions 5.6. Convert Nested Loops into a Single Loop 5.7. Place if-Statements in the Lowest Possible Scope in a Loop Nest 5.8. Declare Variables in the Deepest Scope Possible 5.9. Raise Loop II to Increase fMAX 5.10. Control Loop Interleaving
5.2. Parallelize Loops x
5.2.1. Pipeline Loops 5.2.2. Unroll Loops 5.2.3. Example: Loop Pipelining and Unrolling
6. fMAX Bottleneck Best Practices x
6.1. Balancing Target fMAX and Target II
7. Memory Architecture Best Practices x
7.1. Example: Overriding a Coalesced Memory Architecture 7.2. Example: Overriding a Banked Memory Architecture 7.3. Merge Memories to Reduce Area 7.4. Example: Specifying Bank-Selection Bits for Local Memory Addresses
7.3. Merge Memories to Reduce Area x
7.3.1. Example: Merging Memories Depth-Wise 7.3.2. Example: Merging Memories Width-Wise
8. System of Tasks Best Practices x
8.1. Executing Multiple Loops in Parallel 8.2. Sharing an Expensive Compute Block 8.3. Implementing a Hierarchical Design 8.4. Balancing Capacity in a System of Tasks
8.4. Balancing Capacity in a System of Tasks x
8.4.1. Enable the Intel® HLS Compiler to Infer Data Path Buffer Capacity Requirements 8.4.2. Explicitly Add Buffer Capacity to Your Design When Needed Adding Capacity When Launching Task Functions Adding Capacity When Collecting Task Functions Deciding When To Specify The capacity Parameter
9. Datatype Best Practices x
9.1. Avoid Implicit Data Type Conversions 9.2. Avoid Negative Bit Shifts When Using the ac_int Datatype
10. Advanced Troubleshooting x
10.1. Component Fails Only In Simulation 10.2. Component Gets Poor Quality of Results
1. Intel® HLS Compiler Pro Edition Best Practices Guide
2. Best Practices for Coding and Compiling Your Component
3. FPGA Concepts
4. Interface Best Practices
5. Loop Best Practices
6. fMAX Bottleneck Best Practices
7. Memory Architecture Best Practices
8. System of Tasks Best Practices
9. Datatype Best Practices
10. Advanced Troubleshooting
A. Intel® HLS Compiler Pro Edition Best Practices Guide Archives
B. Document Revision History for Intel® HLS Compiler Pro Edition Best Practices Guide

8.4.2. Explicitly Add Buffer Capacity to Your Design When Needed

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.

Adding Capacity When Launching Task Functions

Consider specifying the capacity parameter of the ihc::launch call if you see stall patterns in your simulation waveforms that indicate an imbalance between the following things:
  • Any back-pressure introduced by the task function
  • How often the caller launches the task function

The figure Data Flow of Multiple Component Invocations Through a System of Tasks in Enable the Intel HLS Compiler to Infer Data Path Buffer Capacity Requirements shows the block diagram of such a design.

Adding Capacity When Collecting Task Functions

Consider specifying the capacity parameter of the ihc::collect call if you see stall patterns in your design waveforms that indicate a difference in the following things:
  • The cadence of data production in the task function
  • The cadence reading that data by the caller function

Deciding When To Specify The capacity Parameter

When you decide whether to add ihc::launch or ihc::collect capacity, ask yourself the following questions:
  • Do any tasks spend cycles stalled waiting for input data to reach them?

    If yes, these tasks require launch capacity equal to the number of cycles it takes input data to reach the task.

    Adding launch capacity allows stalled tasks to buffer their start signals so that they do not stall the other tasks that are scheduled to launch on the same cycle.

    Because the consumer task depends on data from the producer task function, the consumer task stalls until data from the producer task reaches it, so you should add capacity to the ihc::launch call for the consumer task.

  • Do any tasks finish their first execution before the slowest task in the design (that is, the task that produces its initial return signal last) finishes its first execution?

    If yes, the tasks that finish before the slowest task in the design finishes require additional collect capacity.

    Add capacity equal to the number of cycles between when the task produces its first return signal and when the slowest task in the design produces its first return signal.

    When you add collect capacity, tasks can buffer their return signals. Buffering the return signal consumes it from the task, which allows the task to produce more return signals without stalling.

Typically, tasks that communicate only through a return value do not require buffer capacity. The top-level component handles the synchronization of the communication of task functions, as the following figure shows:



When task functions communicate via streams, you might need to add capacity when there is a chance that a task might be launched before its inputs are ready. In the following diagram, you might need to add launch capacity to the consumer task:



For an example of adding buffer capacity to a design, refer the following tutorial: 
<quartus_installdir>/hls/examples/tutorials/system_of_tasks/launch_and_collect_capacity
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