Survey and design of paleozoic: a high-performance compiler tool chain for deep learning inference accelerator

Abstract

Specialized hardware accelerators for deep learning are widely introduced by many hardware vendors because of their high performance and efficiency. However, different vendors adopt different accelerator architectures, making it challenging for the compiler tool-chain to generate and optimize high-performance codes. Moreover, the current tool-chains provided by the vendors are either highly abstract, which makes it hard to optimize or contain too many hardware-related details, which makes it inconvenient to program. So, in this paper, we propose a middle layer compiler tool-chain for Cambricon MLU-100 to fill the gap between high-level runtime library and low operator-level SDK. Our tool-chain is based on the operator level SDK but abstracts away its redundant initialization and allocation statement. We also expose the interface of major optimization knobs compared to the existing runtime, thus enabling a considerable optimization space. We evaluate our work by several state-of-the-art neural networks and choose the line of code and optimization knobs as evaluation metrics. We also compare the performance against state-of-the-art tool-chain TensorRT applying simple optimization strategy and find that our work has great potential in optimization. Our work can guarantee the user a vast optimization space with only around $$ 20\% $$ amount of the codes that hides the redundant initialization and allocation statements from users.