44 citations · 73 across the 10 of their papers we have counts for
4 papers · 1 filter
HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks
James Garland, David Gregg
Convolutional neural networks (CNNs) are typically trained using 16- or 32-bit floating-point (FP) and researchers show that low-precision floating-point (FP) can be highly effecti…
Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization
Yuan Wen, David Gregg
Pruning and quantization are proven methods for improving the performance and storage efficiency of convolutional neural networks (CNNs). Pruning removes near-zero weights in tenso…
TASO: Time and Space Optimization for Memory-Constrained DNN Inference
Yuan Wen, Andrew Anderson, Valentin Radu +2
Convolutional neural networks (CNNs) are used in many embedded applications, from industrial robotics and automation systems to biometric identification on mobile devices. State-of…
Performance-Oriented Neural Architecture Search
Andrew Anderson, Jing Su, Rozenn Dahyot +1
Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems. We argue for the application of the same methodology to…