When Dataflows Converge: Reconfigurable and Approximate Computing for Emerging Neural Networks

Abstract

Deep Neural Networks (DNNs) have gained significant attention in both academia and industry due to the superior application-level accuracy. As DNNs rely on compute- or memory-intensive general matrix multiply (GEMM) operations, approximate computing has been widely explored across the computing stack to mitigate the hardware overheads. However, better-performing DNNs are emerging with growing complexity in their use of nonlinear operations, which incurs even more hardware cost. In this work, we address this challenge by proposing a reconfigurable systolic array to execute both GEMM and nonlinear operations via approximation with distinguished dataflows. Experiments demonstrate that such converging of dataflows significantly saves the hardware cost of emerging DNN inference.

Publication
In IEEE International Conference on Computer Design
Di Wu
Di Wu
PhD student

A Wisconsin Badger in Computer Architecture!

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