Imperial College London > Talks@ee.imperial > CAS Talks > AutoWS: Automate Weights Streaming in Layer-wise Pipelined DNN Accelerators
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AutoWS: Automate Weights Streaming in Layer-wise Pipelined DNN AcceleratorsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact George A Constantinides. 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 This talk is part of the CAS Talks series. This talk is included in these lists:
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