ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a new framework that enables aggressive voltage underscaling of high-performance DNN accelerators without compromising classification accuracy even in the presence of high timing error rates. Using post-synthesis timing simulations of a DNN accelerator modeled on the Google TPU, we show that Thundervolt enables between 34%-57% energy savings on state-of-the-art speech and image recognition benchmarks with less than 1% loss in classification accuracy and no performance loss. Further, we show that Thundervolt is synergistic with and can further increase the energy efficiency of commonly used run-time DNN pruning techniques like Zero-Skip.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Evaluating Built-in ECC of FPGA on-chip Memories for the Mitigation of Undervolting Faults
Voltage underscaling below the nominal level is an effective solution for improving energy efficiency in digital circuits, e.g., Field Programmable Gate Arrays (FPGAs). However, further undervolting below a safe voltage …
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field…
image-classificationImage ClassificationQuantizationOn the Resilience of Deep Learning for Reduced-voltage FPGAs
Deep Neural Networks (DNNs) are inherently computation-intensive and also power-hungry. Hardware accelerators such as Field Programmable Gate Arrays (FPGAs) are a promising solution that can satisfy these requirements fo…
Deep LearningPower and Accuracy of Multi-Layer Perceptrons (MLPs) under Reduced-voltage FPGA BRAMs Operation
In this paper, we exploit the aggressive supply voltage underscaling technique in Block RAMs (BRAMs) of Field Programmable Gate Arrays (FPGAs) to improve the energy efficiency of Multi-Layer Perceptrons (MLPs). Additiona…
Accurate Control under Voltage Drop for Rotor Drones
This letter proposes an anti-disturbance control scheme for rotor drones to counteract voltage drop (VD) disturbance caused by voltage drop of the battery, which is a common case for long-time flight or aggressive maneuv…