pixiesunky / heterocl

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Heterogeneous Computing

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

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Introduction

With the pursuit of improving compute performance under strict power constraints, there is an increasing need for deploying applications to heterogeneous hardware architectures with accelerators, such as GPUs and FPGAs. However, although these heterogeneous computing platforms are becoming widely available, they are very difficult to program especially with FPGAs. As a result, the use of such platforms has been limited to a small subset of programmers with specialized hardware knowledge.

To tackle this challenge, we introduce HeteroCL, a programming infrastructure comprised of a Python-based domain-specific language (DSL) and a compilation flow. The HeteroCL DSL provides a clean programming abstraction that decouples algorithm specification from three important types of hardware customization in compute, data types, and memory architectures. HeteroCL can further capture the interdependence among these different customization techniques, allowing programmers to explore various performance/area/accuracy trade-offs in a systematic and productive manner. In addition, our framework currently provides two advanced domain-specific optimizations with stencil analysis and systolic array generation, which produce highly efficient microarchitectures for accelerating popular workloads from image processing and deep learning domains.

Current Compilation Flow

Evaluation on AWS F1 (Xilinx Virtex UltraScale+^TM VU9P FPGA)

The speedup is over a single-core single-thread CPU execution on AWS F1.

Benchmark	Data Sizes & Type	#LUTs	#FFs	#BRAMs	#DSPs	Freqency (MHz)	Speedup	Back End
KNN Digit Recognition Image classification	K=3 #images=1800 uint49	4009	5835	88	0	250	12.5	General
K-Means Clustering	K=16 #elem=320 x 32 int32	212708	235011	32	1536	190.6	16.0	General
Smith-Waterman Genomic sequencing	string len=128 uint2	110841	88369	1409	0	152.2	20.9	General
Seidel Image processing	2160 pixel x 3840 pixel fixed16	21719	31663	46	96	250	5.9	Stencil
Gaussian Image processing	2160 pixel x 3840 pixel fixed16	70833	131160	46	688	250	13.2	Stencil
Jacobi Linear algebra	2160 pixel x 3840 pixel fixed16	14883	22485	46	48	250	5.0	Stencil
GEMM Matrix multiplication	1024 x 1024 x 1024 fixed16	454492	800283	932	2507	236.8	8.9	Systolic Array
LeNet Inference CNN	MNIST fixed16	362291	660186	739.5	1368	250	10.6	Systolic Array

Publication

If you use HeteroCL in your design, please cite our FPGA'19 paper:

@article{lai2019heterocl,
  title={HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing},
  author={Lai, Yi-Hsiang and Chi, Yuze and Hu, Yuwei and Wang, Jie and Yu, Cody Hao and 
          Zhou, Yuan and Cong, Jason and Zhang, Zhiru},
  journal={Int'l Symp. on Field-Programmable Gate Arrays (FPGA)},
  year={2019}
}

Related Work

HeteroCL is a Python-based DSL extended from TVM and it extends Halide IR for intermediate representation. HeterCL incoporates the SODA framework, PolySA framework, and Merlin Compiler for FPGA back-end generation.

• Stencil with Optimized Dataflow Architecture (SODA)
• Polyhedral-Based Systolic Array Auto-Compilation (PolySA)
• Merlin Compiler
• Halide
• TVM

Contributing to HeteroCL

1. Use Pull Request.
2. Python coding style.
3. Python docstring style.