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

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Date 12/04/2023
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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 Pipelining and Vectorizing a Pipelined Datapath
3.3.3.1.2. Pipelining x
Pipelining and Vectorizing a Pipelined Datapath 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

3.3.3.1.2. Pipelining

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.

Pipelining and Vectorizing a Pipelined Datapath

Consider the following example of code mapping to hardware:

Figure 13. Example Code Mapping to Hardware


Multiple invocations of this code when running on a CPU would not be pipelined. The output of an invocation is completed before inputs are passed to the next invocation of the code.

On an FPGA device, this kind of unpipelined invocation results in poor throughput and low occupancy of the datapath because many of the operations are sitting idle while other parts of the datapath are operating on the data. The following figure shows what throughput and occupancy of invocations looks like in this scenario:
Figure 14. Unpipelined Execution Resulting in Low Throughput and Low Occupancy


The Intel® HLS Compiler pipelines your design as much as possible. New inputs can be sent into the datapath each cycle, giving you a fully occupied datapath for higher throughput, as shown in the following figure:
Figure 15. Pipelining the Datapath Results in High Throughput and High Occupancy


You can gain even further throughput by vectorizing the pipelined hardware. Vectorizing the hardware improves throughput, but requires more FPGA area for the additional copies of the pipelined hardware:
Figure 16. Vectorizing the Pipelined Datapath Resulting in High Throughput and High Occupancy


Understanding where the data you need to pipeline is coming from is key to achieving high performance designs on the FPGA. You can use the following sources of data to take advantage of pipelining:

  • Components
  • Loop iterations

Section Content
Pipelining Loops Within A Component
Pipelining Across Component Invocations

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