Search Results for author:
Found 4 papers, 0 papers with code
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
When integrated with a Tensorcore-based accelerator, APack boosts the speedup and energy efficiency to 1. 44X and 1. 37X respectively.
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time and energy benefits on existing hardware designs that support short bitlengths.
Laconic Deep Learning Computing
A Laconic configuration that uses a 1K-wire weight memory interface, outperforms the 2K-wire conventional accelerator by 15. 4x and is 1. 95x more energy efficient.
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
LM can trade-off accuracy for additional improvements in execution performance and energy efficiency and compares favorably to an accelerator that targeted only activation precisions.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
