MINT implements dynamic-precision CNN inference on FPGA via MSDF digit-serial arithmetic and greedy per-layer precision search, reporting up to 82% higher energy efficiency than INT8 on VGG-16 and ResNet-18 with under 2% accuracy loss.
A review of convolutional neural networks in computer vision
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COMET co-optimizes CNN inference via OBC Schemes A/B on inputs/weights, four LUT techniques, and an im2col-based GEMM core to deliver efficient FPGA deployment with negligible accuracy loss on LeNet-5 and All-CNN-C.
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MINT: Dynamic-Precision CNN Inference with MSDF Digit-Serial Arithmetic on FPGA
MINT implements dynamic-precision CNN inference on FPGA via MSDF digit-serial arithmetic and greedy per-layer precision search, reporting up to 82% higher energy efficiency than INT8 on VGG-16 and ResNet-18 with under 2% accuracy loss.
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COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
COMET co-optimizes CNN inference via OBC Schemes A/B on inputs/weights, four LUT techniques, and an im2col-based GEMM core to deliver efficient FPGA deployment with negligible accuracy loss on LeNet-5 and All-CNN-C.