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arXiv · 2301.07247

Tailor: Altering Skip Connections for Resource-Efficient Inference

Olivia Weng·Gabriel Marcano·Vladimir Loncar·Alireza Khodamoradi·Nojan Sheybani·Andres Meza·Farinaz Koushanfar·Kristof Denolf·Javier Mauricio Duarte·Ryan Kastner
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Abstract

Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and off-chip memory utilization and bandwidth requirements. In this paper, we show that skip connections can be optimized for hardware when tackled with a hardware-software codesign approach. We argue that while a network's skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware efficient implementation with minimal to no accuracy loss. We introduce Tailor, a codesign tool whose hardware-aware training algorithm gradually removes or shortens a fully trained network's skip connections to lower their hardware cost. Tailor improves resource utilization by up to 34% for BRAMs, 13% for FFs, and 16% for LUTs for on-chip, dataflow-style architectures. Tailor increases performance by 30% and reduces memory bandwidth by 45% for a 2D processing element array architecture.

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Olivia Weng, Gabriel Marcano, Vladimir Loncar, Alireza Khodamoradi, Nojan Sheybani, Andres Meza, Farinaz Koushanfar, Kristof Denolf, Javier Mauricio Duarte, Ryan Kastner. 2023-09-15. Tailor: Altering Skip Connections for Resource-Efficient Inference. https://arxiv.org/abs/2301.07247

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