EFFORT: Enhancing Energy Efficiency and Error Resilience of a Near-Threshold Tensor Processing Unit

• Main Authors: Gundi, Noel Daniel, Shabanian, Tahmoures, Basu, Prabal, Pandey, Pramesh, Roy, Sanghamitra, Chakraborty, Koushik, Zhang, Zhen
• Format: Conference Proceeding
• Language: English
• Subjects: Arrays; Clocks; Delays; Energy consumption; Microsoft Windows; Throughput
• ISSN: 2153-697X
• DOI: 10.1109/ASP-DAC47756.2020.9045479

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 by operating the NTC TPU at a substantially high frequency while keeping the voltage at the NTC nominal value. In order to tackle the timing errors due to such aggressive operation, we employ an opportunistic error mitigation strategy. Additionally, we implement an in-situ clock gating architecture, drastically reducing the MACs' dynamic power consumption. Compared to a cutting-edge error mitigation technique for TPUs, EFFORT enables up to 2.5× better performance at NTC with only 2% average accuracy drop across 3 out of 4 DNN datasets.