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 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

Memory Attributes

The following table lists attributes that allow fine-grained control on how you can implement a variable (usually an array) in on-chip memory. The attribute immediately precedes the variable declaration.

FPGA Memory Attributes

Attribute

Syntax

Description

bank_bits

[[intel::bank_bits(b0,b1,...,bn)]]

Specifies that the local memory addresses should use bits (b0,b1,...,bn) for bank selection, where, (b0,b1,...,bn) are indicated in terms of word-addressing and not byte-addressing. As a result, the number of banks is equal to 2<number of bank bits>. The bits of the local memory address not included in (b0,b1,...,bn) are used for word selection in each bank.

bankwidth

[[intel::bankwidth(N)]]

Specifies that the memory system implementing the local variable must have banks that are N bytes wide, where N is a power-of-2 integer value greater than zero.

doublepump

[[intel::doublepump]]

Specifies that the memory system implementing the local variable must operate at twice the clock frequency of the kernel accessing it. This allows twice as many memory accesses per kernel clock cycle but may reduce the maximum kernel clock frequency.

force_pow2_depth

[[intel::force_pow2_depth(N)]]

Specifies that the memory implementing the variable or array has a power-of-2 depth. This attribute is enabled if N is 1, and disabled if N is 0.

max_replicates

[[intel::max_replicates(N)]]

Specifies that the memory implementing the local variable or array has no more than the specified number of replicates to enable simultaneous reads from the datapath.

fpga_memory

[[intel::fpga_memory(“impl_type”)]]

Specifies that the compiler must implement the local variable in a memory system. You may pass an optional string argument to specify the memory implementation type. Specify impl_type as either a BLOCK_RAM or MLAB to implement the memory using memory blocks (for example, M20K) or memory logic array blocks (MLABs), respectively.

merge

[[intel::merge(“key”, “direction”)]]

Merges of two or more variables or arrays defined in the same scope in a width-wise or depth-wise manner. All variables with the same key string are merged into the same memory system. The string direction can be either width or depth.

numbanks

[[intel::numbanks(N)]]

Specifies that the memory system implementing the local variable must have N banks, where N is a power-of-2 integer value greater than zero.

private_copies

[[intel::private_copies(N)]]

Specifies that the memory has a defined number of copies to allow simultaneous iterations of a loop at any given time.

When you declare an array in the scope of a loop body, the array is private to that iteration of the loop. However, concurrency is limited if all iterations of the loop must share the same physical memory. Specifying private_copies(N) allows N iterations of the loop to execute concurrently, each with its own private copy of the array. A larger value of N may expose more opportunities for parallel execution, at a cost of higher on-chip memory utilization.

private_copies(N) interacts with max_concurrency attribute applied to the loop. For more information, refer to the max_concurrency Attribute and FPGA tutorial sample “Private Copies” listed in the Intel® oneAPI Samples Browser on Linux*/Windows* or GitHub.

fpga_register

[[intel::fpga_register]]

Specifies that the variable must be carried through the pipeline in registers. The compiler may implement a register variable either exclusively in flip-flops (FFs), or in a combination of FFs and RAM-based FIFOs.

simple_dual_port

[[intel::simple_dual_port]]

Specifies that the memory implementing the variable or array should have read-only and write-only ports rather than read or write ports. Specifying simple_dual_port forces the compiler to configure the memory's underlying hardware resources in simple dual port mode, which can have area benefits in some corner cases.

singlepump

[[intel::singlepump]]

Specifies that the memory system implementing the local variable must operate at the same clock frequency as the kernel accessing it.

TIP:

For additional information, refer to the FPGA tutorial sample "Memory Attributes" listed in the Intel® oneAPI Samples Browser on Linux* or Windows*, or access the code sample on GitHub.

Struct Data Types and Memory Attributes

You can apply memory attributes to the member variables in a struct variable within the struct declaration. If you also apply memory attributes to the object instantiation of a struct variable, the attributes on the instantiation override the attributes from the declaration.

Consider the following code example where memory attributes are applied to both a declaration and instantiation: 

struct State {
 [[intel::fpga_memory]] int array[100];
 [[intel::fpga_register]] int reg[4];
};

cgh.single_task<class test>([=] {
  struct State S1;
  [[intel::fpga_memory]] struct State S2;
  // some uses
});

In this example code, the compiler splits S1 into two variables, S1.array[100] (implemented in the memory) and S1.reg[4] (implemented in registers). However, the compiler ignores attributes applied at the struct declaration for object S2 and does not split it since the S2 object has the [[intel::fpga_memory]] attribute applied to it.

Parent topic: FPGA Optimization Flags, Attributes, Pragmas, and Extensions
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