AutoWS: Automate Weights Streaming in Layer-wise Pipelined DNN Accelerators

Zhewen Yu; Christos-Savvas Bouganis

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arXiv · 2311.04764

AutoWS: Automate Weights Streaming in Layer-wise Pipelined DNN Accelerators

Zhewen Yu·Christos-Savvas Bouganis

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Abstract

With the great success of Deep Neural Networks (DNN), the design of efficient hardware accelerators has triggered wide interest in the research community. Existing research explores two architectural strategies: sequential layer execution and layer-wise pipelining. While the former supports a wider range of models, the latter is favoured for its enhanced customization and efficiency. A challenge for the layer-wise pipelining architecture is its substantial demand for the on-chip memory for weights storage, impeding the deployment of large-scale networks on resource-constrained devices. This paper introduces AutoWS, a pioneering memory management methodology that exploits both on-chip and off-chip memory to optimize weight storage within a layer-wise pipelining architecture, taking advantage of its static schedule. Through a comprehensive investigation on both the hardware design and the Design Space Exploration, our methodology is fully automated and enables the deployment of large-scale DNN models on resource-constrained devices, which was not possible in existing works that target layer-wise pipelining architectures. AutoWS is open-source: https://github.com/Yu-Zhewen/AutoWS

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Zhewen Yu, Christos-Savvas Bouganis. 2023-11-08. AutoWS: Automate Weights Streaming in Layer-wise Pipelined DNN Accelerators. https://arxiv.org/abs/2311.04764

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