Journal
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
Volume 28, Issue 1, Pages 87-100Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVLSI.2019.2935251
Keywords
Earth Observing System; Radio frequency; Energy consumption; System-on-chip; Memory management; Registers; Random access memory; Convolutional neural network (CNN); dataflow; deep learning; energy-efficient processor; near-threshold voltage (NTV)
Funding
- National Research Foundation of Korea (NRF) - Korean Government (MSIP) [2017R1A2B2009380]
- National Research Foundation of Korea [2017R1A2B2009380] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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We propose a deep convolutional neural network (CNN) inference processor based on a novel enhanced output stationary (EOS) dataflow. Based on the observation that some activations are commonly used in two successive convolutions, the EOS dataflow employs dedicated register files (RFs) for storing such reused activation data to eliminate redundant memory accesses for highly energy-consuming SRAM banks. In addition, processing elements (PEs) are split into multiple small groups such that each group covers a tile of input activation map to increase the usability of activation RFs (ARFs). The processor has two different voltage/frequency domains. The computation domain with 512 PEs operates at near-threshold voltage (NTV) (0.4 V) and 60-MHz frequency to increase energy efficiency, while the rest of the processors including 848-KB SRAMs run at 0.7 V and 120-MHz frequency to increase both on-chip and off-chip memory bandwidths. The measurement results show that our processor is capable of running AlexNet at 831 GOPS/W, VGG-16 at 1151 GOPS/W, ResNet-18 at 1004 GOPS/W, and MobileNet at 948 GOPS/W energy efficiency.
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