ProxSim: GPU-based Simulation Framework for Cross-Layer Approximate DNN Optimization
Cecilia De la Parra1,a, Andre Guntoro1,b and Akash Kumar2,c
1Robert Bosch GmbH Renningen, Germany
acecilia.delaparra@de.bosch.com
bandre.guntoro@de.bosch.com
2Technological University of Dresden Dresden, Germany
cakash.kumar@tu-dresden.de
ABSTRACT
Through cross-layer approximation of Deep Neural Networks (DNN) significant improvements in hardware resources utilization for DNN applications can be achieved. This comes at the cost of accuracy degradation, which can be compensated through different optimization methods. However, DNN optimization is highly time-consuming in existing simulation frameworks for cross-layer DNN approximation, as they are usually implemented for CPU usage only. Specially for largescale image processing tasks, the need of a more efficient simulation framework is evident. In this paper we present ProxSim, a specialized, GPU-accelerated simulation framework for approximate hardware, based on Tensorflow, which supports approximate DNN inference and retraining. Additionally, we propose a novel hardware-aware regularization technique for approximate DNN optimization. By using ProxSim, we report up to 11⨯ savings in execution time, compared to a multi-thread CPU-based framework, and an accuracy recovery of up to 30% for three case studies of image classification with MNIST, CIFAR- 10 and ImageNet.
Keywords: Simulation Framework, Embedded Neural Networks, Approximate Computing, Deep Learning.
