Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous and led to an ever-increasing quest on adopting them from Cloud to edge computing. However, state-of-the-art DNN accelerators pack… Click to show full abstract
Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous and led to an ever-increasing quest on adopting them from Cloud to edge computing. However, state-of-the-art DNN accelerators pack immense computational power in a relatively confined area, inducing significant on-chip power densities that lead to intolerable thermal bottlenecks. Existing state of the art focuses on using approximate multipliers only to trade-off efficiency with inference accuracy. In this work, we present a thermal-aware approximate DNN accelerator design in which we additionally trade-off approximation with temperature effects towards designing DNN accelerators that satisfy tight temperature constraints. Using commercial multi-physics tool flows for heat simulations, we demonstrate how our thermal-aware approximate design reduces the temperature from 139
               
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