Engineering Commons^™

Effort: Enhancing Energy Efficiency And Error Resilience Of A Near-Threshold Tensor Processing Unit, Noel Daniel Gundi, Tahmoures Shabanian, Prabal Basu, Pramesh Pandey, Sanghamitra Roy, Koushik Chakraborty, Zhen Zhang

Electrical and Computer Engineering Faculty Publications

Modern deep neural network (DNN) applications demand a remarkable processing throughput usually unmet by traditional Von Neumann architectures. Consequently, hardware accelerators, comprising a sea of multiplier and accumulate (MAC) units, have recently gained prominence in accelerating DNN inference engine. For example, Tensor Processing Units (TPU) account for a lion's share of Google's datacenter inference operations. The proliferation of real-time DNN predictions is accompanied with a tremendous energy budget. In quest of trimming the energy footprint of DNN accelerators, we propose EFFORT-an energy optimized, yet high performance TPU architecture, operating at the Near-Threshold Computing (NTC) region. EFFORT promotes a better-than-worst-case design …