GreenTPU: Predictive Design Paradigm for Improving Timing Error Resilience of a Near-Threshold Tensor Processing Unit

Pramesh Pandey, Utah State University
Prabal Basu, Utah State University
Koushik Chakraborty, Utah State University
Sanghamitra Roy, Utah State University


The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, the Google tensor processing unit (TPU) has transpired to be the best-in-class, offering more than 15× speedup over the contemporary GPUs. However, the rapid growth in several DNN workloads conspires to escalate the energy consumptions of the TPU-based data-centers. In order to restrict the energy consumption of TPUs, we propose GreenTPU-a low-power near-threshold (NTC) TPU design paradigm. To ensure a high inference accuracy at a low-voltage operation, GreenTPU identifies the patterns in the error-causing activation sequences in the systolic array, and prevents further timing errors from similar patterns by intermittently boosting the operating voltage of the specific multiplier-and-accumulator units in the TPU. Compared to a cutting-edge timing error mitigation technique for TPUs, GreenTPU enables 2× to 3× higher performance (TOPS) in an NTC TPU, with a minimal loss in the prediction accuracy.