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08/16/2023
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design
Graph neural networks (GNNs) have shown significant accuracy improvement...
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06/19/2023
DFlow: Efficient Dataflow-based Invocation Workflow Execution for Function-as-a-Service
The Serverless Computing is becoming increasingly popular due to its eas...
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05/27/2023
AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs
Graph neural networks (GNNs) are powerful tools for exploring and learni...
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05/24/2023
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
The transformer model is known to be computationally demanding, and proh...
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04/15/2023
OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
Transformer-based large language models (LLMs) have achieved great succe...
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04/06/2023
ImaGen: A General Framework for Generating Memory- and Power-Efficient Image Processing Accelerators
Image processing algorithms are prime targets for hardware acceleration ...
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09/22/2022
Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training
An activation function is an element-wise mathematical function and play...
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08/30/2022
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization
Quantization is a technique to reduce the computation and memory cost of...
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08/25/2022
Efficient Activation Quantization via Adaptive Rounding Border for Post-Training Quantization
Post-training quantization (PTQ) attracts increasing attention due to it...
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06/29/2022
SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences
The attention mechanisms of transformers effectively extract pertinent i...
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05/15/2022
Transkimmer: Transformer Learns to Layer-wise Skim
Transformer architecture has become the de-facto model for many machine ...
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02/14/2022
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Quantization of deep neural networks (DNN) has been proven effective for...
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01/17/2022
VELTAIR: Towards High-Performance Multi-tenant Deep Learning Services via Adaptive Compilation and Scheduling
Deep learning (DL) models have achieved great success in many applicatio...
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12/16/2021
Block-Skim: Efficient Question Answering for Transformer
Transformer models have achieved promising results on natural language p...
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10/08/2021
Characterizing and Demystifying the Implicit Convolution Algorithm on Commercial Matrix-Multiplication Accelerators
Many of today's deep neural network accelerators, e.g., Google's TPU and...
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09/08/2021
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection
Federated learning (FL) is a distributed machine learning paradigm that ...
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07/19/2021
ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
Graph neural networks (GNNs) start to gain momentum after showing signif...
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05/20/2021
Dual-side Sparse Tensor Core
Leveraging sparsity in deep neural network (DNN) models is promising for...
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11/11/2020
DLFusion: An Auto-Tuning Compiler for Layer Fusion on Deep Neural Network Accelerator
Many hardware vendors have introduced specialized deep neural networks (...
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11/02/2020
How Far Does BERT Look At:Distance-based Clustering and Analysis of BERT's Attention
Recent research on the multi-head attention mechanism, especially that i...
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09/02/2020
Architectural Implications of Graph Neural Networks
Graph neural networks (GNN) represent an emerging line of deep learning ...
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08/29/2020
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
Network pruning can reduce the high computation cost of deep neural netw...
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08/23/2020
Ptolemy: Architecture Support for Robust Deep Learning
Deep learning is vulnerable to adversarial attacks, where carefully-craf...
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06/01/2020
Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins
Modern large-scale computing systems (data centers, supercomputers, clou...
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05/05/2020
Towards QoS-Aware and Resource-Efficient GPU Microservices Based on Spatial Multitasking GPUs In Datacenters
While prior researches focus on CPU-based microservices, they are not ap...
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02/18/2020
Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integration
The research interest in specialized hardware accelerators for deep neur...
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04/17/2019