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

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Date 3/28/2022
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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
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
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

4.1.6. Pass-by-Value Interface

For software developers accustomed to writing code that targets a CPU, passing each element in an array by value might be unintuitive because it typically results in many function calls or large parameters. However, for code that targets an FPGA device, passing array elements by value can result in smaller and simpler hardware on the FPGA device.

The vector addition example can be coded to pass the vector array elements by value as follows. A struct is used to pass the entire array (of 8 data elements) by value.

When you use a struct to pass the array, you must also do the following things:
  • Define element-wise copy constructors.
  • Define element-wise copy assignment operators.
  • Add the hls_register memory attribute to all struct members in the definition. 
struct int_v8 {
    hls_register int data[8];

    //copy assignment operator 
    int_v8 operator=(const int_v8& org) {
         #pragma unroll
         for (int i=0; i< 8; i++) {
             data[i]       = org.data[i]      ;       
         }
            return *this;
    }
    
    //copy constructor
    int_v8 (const int_v8& org) {
         #pragma unroll
         for (int i=0; i< 8; i++) {
             data[i]       = org.data[i]      ;       
         }
    }

    //default construct & destructor 
    int_v8() {}; 
    ~int_v8() {};
};

component int_v8 vector_add(
    int_v8 a,
    int_v8 b) {
    int_v8 c;
    #pragma unroll 8
    for (int i = 0; i < 8; ++i) {
    c.data[i] = a.data[i]
    + b.data[i];
    }
    return c;
}

This component takes and processes only eight elements of vector a and vector b, and returns eight elements of vector c. To compute 1024 elements for the example, the component needs to be called 128 times (1024/8). While in previous examples the component contained loops that were pipelined, here the component is invoked many times, and each of the invocations are pipelined.

The following diagram shows the Function View in the System Viewer that is generated when you compile this example.
Figure 28. System Viewer Function View of vector_add Component with Pass-By-Value Interface


The latency of this component is one, and it has a loop initiation interval (II) of one.
Compiling this component with an Intel® Quartus® Prime compilation flow targeting an Intel® Arria® 10 device results in the following QoR metrics:
Table 6.  QoR Metrics Comparison for Pass-by-Value Interface1
QoR Metric Pointer Avalon® MM Host Avalon® MM Agent Avalon® ST Pass-by-Value
ALMs 15593.5 643 490.5 314.5 130
DSPs 0 0 0 0 0
RAMs 30 0 48 0 0
fMAX (MHz)2 298.6 472.37 498.26 389.71 581.06
Latency (cycles) 24071 142 139 134 128
Initiation Interval (II) (cycles) ~508 1 1 1 1
1The compilation flow used to calculate the QoR metrics used Intel® Quartus® Prime Pro Edition Version 17.1.
2The fMAX measurement was calculated from a single seed.
The QoR metrics for the vector_add component with a pass-by-value interface shows fewer ALM used, a high component fMAX, and optimal values for latency and II. In this case, the II is the same as the component invocation interval. A new invocation of the component can be launched every clock cycle. With an initiation interval of 1, 128 component calls are processed in 128 cycles so the overall latency is 128.
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