A 119.64 GOPs/W FPGA-Based ResNet50 Mixed-Precision Accelerator Using the Dynamic DSP Packing

被引:2
作者
Ou, Yaozhong [1 ]
Yu, Wei-Han [1 ]
Un, Ka-Fai [1 ]
Chan, Chi-Hang [1 ]
Zhu, Yan [1 ]
机构
[1] Univ Macau, State Key Lab Analog & Mixed Signal VLSI, Macau, Peoples R China
基金
中国国家自然科学基金;
关键词
Quantization (signal); Sensitivity; Throughput; Bandwidth; Computational modeling; Clocks; Convolutional neural network (CNN); mixed-precision quantization; field programmable gate array (FPGA); digital signal processor (DSP); image classification; CONVOLUTIONAL NEURAL-NETWORKS;
D O I
10.1109/TCSII.2024.3377356
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This brief presents a precision-sensitivity-aware quantization (PSAQ) mixed precision (MP) compression scheme designed for both weights and activations. The PSAQ MP method achieves a better trade-off between accuracy and energy efficiency, maintaining 75.6% top-1 accuracy in ResNet-50 and achieving 2.06 x reduction in normalized operation with less than 1% accuracy difference compared to baseline. We propose two DSP-pipeline-friendly methods, dynamic DSP packing (DDP) and fully pre-calibrated (FPC) unpacking, to pack multiple operations into single DSP in error-free style with only one more clock cycle and slight logic overhead compared to the one without packing, by which the accelerator can simultaneously address the support for MP algorithms and efficient utilization of DSP bandwidth. Cooperated by the router network and optimized dataflow, our MP accelerator achieves 330.15 GOP/s throughput and 119.64 GOPs/W energy efficiency under 2.27-b weight and 3.61-b input feature map (ifmap).
引用
收藏
页码:2554 / 2558
页数:5
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