Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration

• URL: http://arxiv.org/abs/2011.13000v3
• Date: Thu, 28 Oct 2021 21:14:15 GMT
• Title: Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration
• Authors: Reena Elangovan, Shubham Jain, Anand Raghunathan
• Abstract summary: Precision scaling has emerged as a popular technique to optimize the compute and storage requirements of Deep Neural Networks (DNNs) Efforts toward creating ultra-low-precision (sub-8-bit) DNNs suggest that the minimum precision required to achieve a given network-level accuracy varies considerably across networks. Previous proposals such as bit-serial hardware incur high overheads, significantly diminishing the benefits of lower precision.
• Score: 3.7371886886933487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precision scaling has emerged as a popular technique to optimize the compute and storage requirements of Deep Neural Networks (DNNs). Efforts toward creating ultra-low-precision (sub-8-bit) DNNs suggest that the minimum precision required to achieve a given network-level accuracy varies considerably across networks, and even across layers within a network, requiring support for variable precision in DNN hardware. Previous proposals such as bit-serial hardware incur high overheads, significantly diminishing the benefits of lower precision. To efficiently support precision re-configurability in DNN accelerators, we introduce an approximate computing method wherein DNN computations are performed block-wise (a block is a group of bits) and re-configurability is supported at the granularity of blocks. Results of block-wise computations are composed in an approximate manner to enable efficient re-configurability. We design a DNN accelerator that embodies approximate blocked computation and propose a method to determine a suitable approximation configuration for a given DNN. By varying the approximation configurations across DNNs, we achieve 1.17x-1.73x and 1.02x-2.04x improvement in system energy and performance respectively, over an 8-bit fixed-point (FxP8) baseline, with negligible loss in classification accuracy. Further, by varying the approximation configurations across layers and data-structures within DNNs, we achieve 1.25x-2.42x and 1.07x-2.95x improvement in system energy and performance respectively, with negligible accuracy loss.

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