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07/11/2022
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
Sparse tensors are rapidly becoming critical components of modern deep l...
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10/28/2021
Characterizing and Taming Resolution in Convolutional Neural Networks
Image resolution has a significant effect on the accuracy and computatio...
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05/28/2021
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
ML workloads are becoming increasingly popular in the cloud. Good cloud ...
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05/19/2021
Pure Tensor Program Rewriting via Access Patterns (Representation Pearl)
Tensor kernels in machine learning (ML) often correspond to pure mathema...
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04/21/2021
Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
Graph neural networks (GNNs), an emerging deep learning model class, can...
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03/30/2021
VSS: A Storage System for Video Analytics [Technical Report]
We present a new video storage system (VSS) designed to decouple high-le...
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03/27/2021
Automated Backend-Aware Post-Training Quantization
Quantization is a key technique to reduce the resource requirement and i...
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11/29/2020
Srift: Swift and Thrift Cloud-Based Distributed Training
Cost-efficiency and training time are primary concerns in cloud-based di...
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02/29/2020
Enumerating Hardware-Software Splits with Program Rewriting
A core problem in hardware-software codesign is in the sheer size of the...
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02/04/2019
Vignette: Perceptual Compression for Video Storage and Processing Systems
Compressed videos constitute 70 rates far outpace compute and storage im...
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10/25/2018
Automating Generation of Low Precision Deep Learning Operators
State of the art deep learning models have made steady progress in the f...
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10/05/2018
Computer Security Risks of Distant Relative Matching in Consumer Genetic Databases
Consumer genetic testing has become immensely popular in recent years an...
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07/11/2018
VTA: An Open Hardware-Software Stack for Deep Learning
Hardware acceleration is an enabler for ubiquitous and efficient deep le...
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05/21/2018
Learning to Optimize Tensor Programs
We introduce a learning-based framework to optimize tensor programs for ...
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05/21/2018
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Distributed deep neural network (DDNN) training constitutes an increasin...
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03/01/2018
Correlation Manipulating Circuits for Stochastic Computing
Stochastic computing (SC) is an emerging computing technique that promis...
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02/12/2018
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
There is an increasing need to bring machine learning to a wide diversit...
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02/12/2018
TVM: End-to-End Optimization Stack for Deep Learning
Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive...
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01/30/2018
Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training
Most work in the deep learning systems community has focused on faster i...
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06/14/2017
MATIC: Adaptation and In-situ Canaries for Energy-Efficient Neural Network Acceleration
- The primary author has withdrawn this paper due to conflict of interes...
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06/12/2017
Exploring Computation-Communication Tradeoffs in Camera Systems
Cameras are the defacto sensor. The growing demand for real-time and low...
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06/07/2017
Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing
Recent advances in neural networks (NNs) exhibit unprecedented success a...
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12/09/2016
Arch2030: A Vision of Computer Architecture Research over the Next 15 Years
Application trends, device technologies and the architecture of systems ...
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06/12/2016