Automatic End-to-End Joint Optimization for Kernel Compilation on DSPs

Digital signal processors (DSPs) commonly adopt VLIW-SIMD architecture and are extensively applied in most compute-heavy embedded sensing applications. The performances for DSP kernels rely heavily on compilations and handwritten optimizations. Hand-crafted methods suffer from heavy burden on progra...

Full description

Saved in:
Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Zhao, Xiaolei, Chen, Zhaoyun, Shi, Yang, Wen, Mei, Zhang, Chunyun
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
Auto Tuning
Code Generation
Codes
Digital signal processors
DSP
Generators
Kernel Compilation
Q-learning
Reinforcement Learning
Sensors
Signal processing algorithms
Space exploration
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Digital signal processors (DSPs) commonly adopt VLIW-SIMD architecture and are extensively applied in most compute-heavy embedded sensing applications. The performances for DSP kernels rely heavily on compilations and handwritten optimizations. Hand-crafted methods suffer from heavy burden on programmers, while state-of-the-art automatic compilation methods always focus more on a certain aspect (tiling or auto-vectorization), lacking of global and sequential vision on the intact compilation optimization process. It still requires empirical adjustments by programmers in the actual scenario.In order to release programmers from kernel tuning, we propose JOKer, an automatic end-to-end multi-level code generator for kernel joint optimization on DSPs. JOKer integrates means of optimizations in compiling process and provides an end-to-end workflow for performance tuning. It explores compilation configurations through a reinforcement learning based agent for global optimal solution and generates high performance kernel codes for DSPs automatically.
DOI:10.1109/DAC56929.2023.10247901