151 citations · 437 across the 32 of their papers we have counts for
15 papers · 1 filter
Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training
Hongzheng Chen, Cody Hao Yu, Shuai Zheng +3
Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off…
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Tao Yu, Yichi Zhang, Zhiru Zhang +1
Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with high dimensional binary vectors. It is attractive because of its energy efficiency and low late…
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Chenhui Deng, Xiuyu Li, Zhuo Feng +1
Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data. However, recent studies show that GNNs are vulnera…
PokeBNN: A Binary Pursuit of Lightweight Accuracy
Yichi Zhang, Zhiru Zhang, Lukasz Lew
Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the…
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua, Yichi Zhang, Chuan Guo +2
Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common cor…
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao +3
A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model. Our approach, named SPADE, exploits bijective distance m…
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
Yichi Zhang, Junhao Pan, Xinheng Liu +3
Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory requ…
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
Shubham Rai, Walter Lau Neto, Yukio Miyasaka +37
Logic synthesis is a fundamental step in hardware design whose goal is to find structural representations of Boolean functions while minimizing delay and area. If the function is c…
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Yuwei Hu, Zihao Ye, Minjie Wang +6
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is a…
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators
Ritchie Zhao, Jordan Dotzel, Zhanqiu Hu +3
Outliers in weights and activations pose a key challenge for fixed-point quantization of neural networks. While they can be addressed by fine-tuning, this is not practical for ML s…
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang +2
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding m…
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Cunxi Yu, Zhiru Zhang
Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step. Demands for accurate routing quality prediction raise to…
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature foc…
Building Efficient Deep Neural Networks with Unitary Group Convolutions
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
We propose unitary group convolutions (UGConvs), a building block for CNNs which compose a group convolution with unitary transforms in feature space to learn a richer set of repre…
Channel Gating Neural Networks
Weizhe Hua, Yuan Zhou, Christopher De Sa +2
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channe…