Date: 2020. 09.21 (Mon) 14:00
Locate: EB5. 533
Presenter: Jaemin Kang
Title: Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
Author: Eriko Nurvitadhi, Ganesh Venkatesh, Jaewoong Sim, Debbie Marr, Randy Huang, Jason Gee Hock Ong, Yeong Tat Liew, Krishnan Srivatsan3, Duncan Moss3, Suchit Subhaschandra3, Guy Boudoukh
Abstract: Current-generation Deep Neural Networks (DNNs), such as AlexNet and VGG, rely heavily on dense floating-point matrix multiplication (GEMM), which maps well to GPUs (regular parallelism, high TFLOP/s). Because of this, GPUs are widely used for accelerating DNNs. Current FPGAs offer superior energy efficiency (Ops/Watt), but they do not offer the performance of today’s GPUs on DNNs. In this paper, we look at upcoming FPGA technology advances, the rapid pace of innovation in DNN algorithms, and consider whether future high-performance FPGAs will outperform GPUs for next-generation DNNs. The upcoming Intel® 14-nm StratixTM 10 FPGAs will have thousands of hard floating-point units (DSPs) and on-chip RAMs (M20K memory blocks). They will also have high bandwidth memories (HBMs) and improved frequency (HyperFlex™ core architecture). This combination of features brings FPGA raw floating point performance within striking distance of GPUs. Meanwhile, DNNs are quickly evolving. For example, recent innovations that exploit sparsity (e.g., pruning) and compact data types (e.g., 1-2 bit) result in major leaps in algorithmic efficiency. However, these innovations introduce irregular parallelism on custom data types, which are difficult for GPUs to handle but would be a great fit for FPGA’s extreme customizability. This paper evaluates a selection of emerging DNN algorithms on two generations of Intel FPGAs (ArriaTM 10, StratixTM 10) against the latest highest performance Titan X Pascal GPU. We created a customizable DNN accelerator template for FPGAs and used it in our evaluations. First, we study various GEMM operations for next-generation DNNs. Our results show that Stratix 10 FPGA is 10%, 50%, and 5.4x better in performance (TOP/sec) than Titan X Pascal GPU on GEMM operations for pruned, Int6, and binarized DNNs, respectively. Then, we present a detailed case study on accelerating Ternary ResNet which relies on sparse GEMM on 2-bit weights (i.e., weights constrained to 0,+1,-1) and full-precision neurons. The Ternary ResNet accuracy is within ~1% of the fullprecision ResNet which won the 2015 ImageNet competition. On Ternary-ResNet, the Stratix 10 FPGA can deliver 60% better performance over Titan X Pascal GPU, while being 2.3x better in performance/watt. Our results indicate that FPGAs may become the platform of choice for accelerating next-generation DNNs.
Article source: //eslab.cnu.ac.kr/en/Mobile/208-Can-FPGAs-Beat-GPUs-in-Accelerating-Next-Generation-Deep-Neural-Networks.html