233 citations · 833 across the 22 of their papers we have counts for
6 papers · 1 filter
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj +3
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchi…
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM
Siming Ma, David Brooks, Gu-Yeon Wei
We propose a new algorithm for training neural networks with binary activations and multi-level weights, which enables efficient processing-in-memory circuits with embedded nonvola…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Thierry Tambe, En-Yu Yang, Zishen Wan +5
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequate…
MASR: A Modular Accelerator for Sparse RNNs
Udit Gupta, Brandon Reagen, Lillian Pentecost +5
Recurrent neural networks (RNNs) are becoming the de facto solution for speech recognition. RNNs exploit long-term temporal relationships in data by applying repeated, learned tran…
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
Yu Emma Wang, Gu-Yeon Wei, David Brooks
Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep lear…