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07/27/2023
Speeding up Fourier Neural Operators via Mixed Precision
The Fourier neural operator (FNO) is a powerful technique for learning s...
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01/27/2023
TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization
Stream processing engines (SPEs) are widely used for large scale streami...
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10/19/2022
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
Training deep learning models can be computationally expensive. Prior wo...
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10/18/2022
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
As deep learning models nowadays are widely adopted by both cloud servic...
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07/15/2022
Optimizing Data Collection in Deep Reinforcement Learning
Reinforcement learning (RL) workloads take a notoriously long time to tr...
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06/18/2021
Distributed Deep Learning in Open Collaborations
Modern deep learning applications require increasingly more compute to t...
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03/04/2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
Training deep neural networks on large datasets can often be accelerated...
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02/08/2021
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
Deep reinforcement learning (RL) has made groundbreaking advancements in...
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02/03/2021
Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models
Driven by the tremendous effort in researching novel deep learning (DL) ...
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01/31/2021
A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
Deep learning researchers and practitioners usually leverage GPUs to hel...
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12/01/2020
LifeStream: A High-performance Stream Processing Engine for Waveform Data
Hospitals around the world collect massive amount of physiological data ...
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11/02/2020
IOS: Inter-Operator Scheduler for CNN Acceleration
To accelerate CNN inference, existing deep learning frameworks focus on ...
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10/15/2020
FPRaker: A Processing Element For Accelerating Neural Network Training
We present FPRaker, a processing element for composing training accelera...
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09/01/2020
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
TensorDash is a hardware level technique for enabling data-parallel MAC ...
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08/15/2020
Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
Training a state-of-the-art deep neural network (DNN) is a computational...
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08/01/2020
Multi-node Bert-pretraining: Cost-efficient Approach
Recently, large scale Transformer-based language models such as BERT, GP...
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07/02/2020
Automatic Horizontal Fusion for GPU Kernels
We present automatic horizontal fusion, a novel optimization technique t...
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06/05/2020
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training
Modern deep neural network (DNN) training jobs use complex and heterogen...
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11/06/2019
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning....
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10/02/2019
MLPerf Training Benchmark
Machine learning is experiencing an explosion of software and hardware s...
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07/23/2019
BPPSA: Scaling Back-propagation by Parallel Scan Algorithm
In an era when the performance of a single compute device plateaus, soft...
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07/23/2019
Scaling Back-propagation by Parallel Scan Algorithm
In an era when the performance of a single compute device plateaus, soft...
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05/10/2019
Priority-based Parameter Propagation for Distributed DNN Training
Data parallel training is widely used for scaling distributed deep neura...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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01/04/2019
StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory
Stream analytics have an insatiable demand for memory and performance. E...
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05/22/2018
EcoRNN: Fused LSTM RNN Implementation with Data Layout Optimization
Long-Short-Term-Memory Recurrent Neural Network (LSTM RNN) is a state-of...
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05/08/2018
Exploiting Row-Level Temporal Locality in DRAM to Reduce the Memory Access Latency
This paper summarizes the idea of ChargeCache, which was published in HP...
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05/08/2018
SoftMC: Practical DRAM Characterization Using an FPGA-Based Infrastructure
This paper summarizes the SoftMC DRAM characterization infrastructure, w...
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05/08/2018
Flexible-Latency DRAM: Understanding and Exploiting Latency Variation in Modern DRAM Chips
This article summarizes key results of our work on experimental characte...
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05/07/2018
RowClone: Accelerating Data Movement and Initialization Using DRAM
In existing systems, to perform any bulk data movement operation (copy o...
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05/04/2018
Adaptive-Latency DRAM: Reducing DRAM Latency by Exploiting Timing Margins
This paper summarizes the idea of Adaptive-Latency DRAM (AL-DRAM), which...
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05/02/2018
Decoupling GPU Programming Models from Resource Management for Enhanced Programming Ease, Portability, and Performance
The application resource specification--a static specification of severa...
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03/16/2018
TBD: Benchmarking and Analyzing Deep Neural Network Training
The recent popularity of deep neural networks (DNNs) has generated a lot...
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02/07/2018
Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management
The application resource specification--a static specification of severa...
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10/30/2016
Understanding and Exploiting Design-Induced Latency Variation in Modern DRAM Chips
Variation has been shown to exist across the cells within a modern DRAM ...
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09/07/2016
Practical Data Compression for Modern Memory Hierarchies
In this thesis, we describe a new, practical approach to integrating har...
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03/28/2016
