Developer Guide

FPGA Optimization Guide for Intel® oneAPI Toolkits

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ID 767853
Date 3/31/2023
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Document Table of Contents
Document Table of Contents x
FPGA Optimization Guide for Intel® oneAPI Toolkits Introduction To FPGA Design Concepts Analyze Your Design Optimize Your Design FPGA Optimization Flags, Attributes, Pragmas, and Extensions Quick Reference Additional Information Document Revision History for the FPGA Optimization Guide for Intel® oneAPI Toolkits
Introduction To FPGA Design Concepts x
FPGA Architecture Overview Concepts of FPGA Hardware Design Methods of Hardware Design How Source Code Becomes a Custom Hardware Datapath Scheduling Mapping Parallelism Models to FPGA Hardware Memory Types
FPGA Architecture Overview x
Adaptive Logic Module (ALM) Lookup Table (LUT) Register Digital Signal Processing (DSP) Block Random Access Memory (RAM) Blocks
Concepts of FPGA Hardware Design x
Maximum Frequency (fMAX) Latency Pipelining Throughput Datapath Control Path Occupancy
How Source Code Becomes a Custom Hardware Datapath x
Mapping Source Code Instructions to Hardware Mapping Arrays and Their Accesses to Hardware
Scheduling x
Dynamic Scheduling Clustering the Datapath Handshaking Between Clusters
Mapping Parallelism Models to FPGA Hardware x
Data Parallelism Task Parallelism
Data Parallelism x
Executing Independent Operations Simultaneously Pipelining
Memory Types x
Kernel Memory Global Memory
Analyze Your Design x
Analyze the FPGA Early Image Analyze the FPGA Image
Analyze the FPGA Early Image x
Review the FPGA Optimization Report Access HLD FPGA Reports in JSON Format
Review the FPGA Optimization Report x
Loop Analysis Bottlenecks Viewer Area Estimates System Viewer Kernel Memory Viewer Schedule Viewer
Analyze the FPGA Image x
Quartus (Static) Summary Intel® FPGA Dynamic Profiler for DPC++ System-level Profiling Using the Intercept Layer for OpenCL* Applications
Quartus (Static) Summary x
Timing Failures
Intel® FPGA Dynamic Profiler for DPC++ x
Measure Kernel Performance Instrument the Kernel Pipeline with Performance Counters (-Xsprofile) Obtain Profiling Data During Runtime Reduce Area Resource Use While Profiling Profiler Analyses of Example SYCL* Design Scenarios Limitations
Obtain Profiling Data During Runtime x
Invoke the Profiler Runtime Wrapper to Obtain Profiling Data Use Intel® VTune™ Profiler
Use Intel® VTune™ Profiler x
Interpret Performance Counter Data
System-level Profiling Using the Intercept Layer for OpenCL* Applications x
Set Up the Intercept Layer for OpenCL* Applications
Optimize Your Design x
Throughput Resource Use
Throughput x
Single Work-item Kernels NDRange Kernels Memory Accesses Pipes Host
Single Work-item Kernels x
Single Work-item Kernel Design Guidelines Loops Single-Cycle Floating-Point Accumulator for Single Work-Item Kernels
Loops x
Refactor the Loop-Carried Data Dependency Relax Loop-Carried Dependency Transfer Loop-Carried Dependency to Local Memory Minimize the Memory Dependencies for Loop Pipelining Unroll Loops Fuse Loops to Reduce Overhead and Improve Performance Optimize Loops With Loop Speculation Remove Loop Bottlenecks Shannonization to Improve FMAX/II Optimize Inner Loop Throughput Improve Loop Performance by Caching On-Chip Memory
Single-Cycle Floating-Point Accumulator for Single Work-Item Kernels x
Strategies for Inferring the Accumulator
Memory Accesses x
Load-Store Units Global Memory Accesses Optimization Perform Kernel Computations Using Local or Private Memory Local and Private Memory Accesses Optimization Annotating Unified Shared Memory Pointers Zero-Copy Memory Access Additional Recommendations
Load-Store Units x
Load-Store Unit Styles Load-Store Unit Modifiers Load-Store Unit Controls
Global Memory Accesses Optimization x
Global Memory Bandwidth Use Calculation Manual Partition of Global Memory Partitioning Buffers Across Different Memory Types (Heterogeneous Memory) Partitioning Buffers Across Memory Channels of the Same Memory Type Ignoring Dependencies Between Accessor Arguments Contiguous Memory Accesses Static Memory Coalescing
Host x
Multi-Threaded Host Application Utilizing Hardware Kernel Invocation Queue Double Buffering Host Utilizing Kernel Invocation Queue N-Way Buffering to Overlap Kernel Execution Prepinning Memory Simple Host-Device Streaming Buffered Host-Device Streaming
Double Buffering Host Utilizing Kernel Invocation Queue x
Applying Double-Buffering Using the Intercept Layer for OpenCL* Applications
Resource Use x
Data Types and Operations Kernel Variable Accesses
Data Types and Operations x
Optimize Floating-point Operation Avoid Expensive Functions Variable-Precision Integer and Floating-Point Support
Variable-Precision Integer and Floating-Point Support x
Advantages and Limitations of Arbitrary Precision Data Types Declare and Use the AC Data Types
Declare and Use the AC Data Types x
Declare the ac_int Data Type Declare the ac_fixed Data Type Declare the ac_complex Data Type Declare the ap_float Data Type
Declare the ap_float Data Type x
Conversion Rules for ap_float Operations with Explicit Precision Controls Rounding Mode Control For ap_float to ap_float Conversions Multiplication Addition/Subtraction/Division Comparison Operators Additional ap_float Functions Additional Data Types Provided by the ap_float.hpp Header File Quality of Results and the ap_float Data Type
FPGA Optimization Flags, Attributes, Pragmas, and Extensions x
Optimization Flags Optimization Targets Kernel Variables Kernel Attributes Memory Attributes Loop Directives Floating-Point Pragmas Latency Controls (Beta) FPGA Extensions
Optimization Flags x
Specify Schedule FMAX Target for Kernels (-Xsclock=<clock target>) Disable Burst-Interleaving of Global Memory (-Xsno-interleaving=<global_memory_type>) Force Ring Interconnect for Global Memory (-Xsglobal-ring) Force a Single Store Ring to Reduce Area (-Xsforce-single-store-ring) Force Fewer Read Data Reorder Units to Reduce Area (-Xsnum-reorder) Disable Hardware Kernel Invocation Queue (-Xsno-hardware-kernel-invocation-queue) Modify the Handshaking Protocol Between Clusters (-Xshyper-optimized-handshaking) Disable Automatic Fusion of Loops (-Xsdisable-auto-loop-fusion) Fuse Adjacent Loops With Unequal Trip Counts (-Xsenable-unequal-tc-fusion) Pipeline Loops in Non-task Kernels (-Xsauto-pipeline) Control Semantics of Floating-Point Operations (-fp-model=<value>) Modify the Rounding Mode of Floating-point Operations (-Xsrounding=<rounding_type>) Global Control of Exit FIFO Latency of Stall-free Clusters (-Xssfc-exit-fifo-type=<value>) Enable the Read-Only Cache for Read-Only Accessors (-Xsread-only-cache-size=<N>) Control Hardware Implementation of the Supported Data Types and Math Operations (-Xsdsp-mode=<option>)
Optimization Targets x
Minimum Latency Flow
Kernel Attributes x
Specify Schedule FMAX Target for Kernels Specify a Workgroup Size Specify Number of SIMD Work Items Omit Hardware that Generates and Dispatches Kernel IDs Omit Hardware to Support the no_global_work_offset Attribute in parallel_for Kernels Reduce Kernel Area and Latency
Loop Directives x
disable_loop_pipelining Attribute initiation_interval Attribute ivdep Attribute loop_coalesce Attribute max_concurrency Attribute max_interleaving Attribute speculated_iterations Attribute unroll Pragma Loop Fuse Functions and nofusion Attribute max_reinvocation_delay Attribute (Beta)
FPGA Extensions x
Pipes Extension System of Tasks Extension (task_sequence) device_global Extension (Beta)
Pipes Extension x
Key Properties of a Pipe Accessing Pipes The pipe Class and its Use I/O Pipes Characteristics of Pipes Restrictions of Pipes Guidelines for Designing Pipes Pipe and Atomic Fence
System of Tasks Extension (task_sequence) x
Task Functions task_sequence Use Cases
Quick Reference x
Algorithmic C Data Types Floating Point Pragmas FPGA Accessor Properties FPGA Extensions FPGA Kernel Attributes FPGA Local Memory Function Latency Control Properties (Beta) FPGA LSU Controls FPGA Loop Directives FPGA Memory Attributes FPGA Optimization Flags Pipe API
Document Revision History for the FPGA Optimization Guide for Intel® oneAPI Toolkits x
Notices and Disclaimers
FPGA Optimization Guide for Intel® oneAPI Toolkits
Introduction To FPGA Design Concepts
Analyze Your Design
Optimize Your Design
FPGA Optimization Flags, Attributes, Pragmas, and Extensions
Quick Reference
Additional Information
Document Revision History for the FPGA Optimization Guide for Intel® oneAPI Toolkits

Operations with Explicit Precision Controls

The following operations leave the precision of the input ap_float-type variables untouched and allow you to control the output precision:

Rounding Mode Control For ap_float to ap_float Conversions

Syntax

convert_to<output_exponent_width, output_mantissa_width, rounding_mode>

Description

Use this method to override the rounding mode set for an ap_float variable when converting the variable to a different precision. By default, ap_float to ap_float conversions use the rounding mode that you specified when you declared the variable.

Multiplication

Syntax

ihc::ap_float<output_exponent_width, output_mantissa_width>::mul <[accuracy_setting], [subnormal_setting]> (ap_float_a, ap_float_b)

Description

This math function supplements the basic multiplication operation performed by the multiplication (*) operator. Multiplies ap_float_a and ap_float_b without changing the input types and outputs an ap_float at the specified precision. The optional parameters are defined as follows:

  • subnormal_setting: Optional parameter to specify whether input and output numbers are flushed to zero when carrying out basic binary operations explicitly. Set this parameter with one of the following values:
    subnormal_setting Parameter Values
    subnormal_setting Values Description
    ihc::fp_config::FP_Subnormal::ON Input and output numbers in the subnormal range are preserved. The target FPGA device must have subnormal support. Subnormal support might require more FPGA area.
    ihc::fp_config::FP_Subnormal::OFF Input or output numbers in the subnormal range are flushed to zero.
    ihc::fp_config::FP_Subnormal::AUTO With this setting, the Intel® oneAPI DPC++/C++ Compiler enables subnormal support only under the folllowing conditions:
    • The target FPGA device directly supports it.
    • It does not incur any extra FPGA area overhead.

    If you do not set the subnormal_setting parameter, the ihc::fp_config::FP_Subnormal::AUTO subnormal setting is used by default.

  • accuracy_setting: Optional parameter that influences trade-offs between the accuracy of the result due to different rounding decisions in the intermediary calculations and the FPGA area utilized by the generated hardware. Floating-point operations with less accurate results typically use fewer logic elements. For example, a divider with a high accuracy might use 20% more FPGA area than a divider with low accuracy. The low accuracy divider has a higher error bound [1 unit of least precision (ULP)] than a high accuracy divider (0.5 ULP).

    Set this parameter with one of the following values:

    accuracy_setting Parameter Values
    accuracy_setting Values Description
    ihc::fp_config::FP_Accuracy::HIGH

    Uses the high precision version of the floating-point math operations. This is the default setting.

    ihc::fp_config::FP_Accuracy::LOW Allows the compiler to use a higher error bound to save on-chip area.

    If you do not set the accuracy_setting parameter, ihc::fp_config::FP_Accuracy::HIGH accuracy setting is used by default.

Addition/Subtraction/Division

Syntax

ihc::ap_float<output_exponent_width, output_mantissa_width>::add <[optional parameters]> (ap_float_a, ap_float_b) 
ihc::ap_float<output_exponent_width, output_mantissa_width>::sub <[optional parameters]> (ap_float_a, ap_float_b) 
ihc::ap_float<output_exponent_width, output_mantissa_width>::div <[optional parameters]> (ap_float_a, ap_float_b)

Description

These math functions supplement the basic math operations performed by the addition/subtraction/division (+ / − //) operators. Adds/subtracts/divides ap_float_a and ap_float_b by first casting ap_float_a and ap_float_b to the specified ap_float precision. The operation and output are at the specified precision.

You can also specify the optional parameters accuracy_setting and subnormal_setting described earlier.

NOTE:

If your SYCL code performs computations on constant/literal native floating-point values, the compiler can sometimes combine them at compile time and save area. This is a compiler optimization technique called constant folding or constant propagation. This optimization does not work for ap_float even when the operands are constant. You should compute your constant arithmetic in native types or precompute manually.

Parent topic: Declare the ap_float Data Type
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