Search Results for author:
Found 26 papers, 10 papers with code
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
With the proposed integer quantization search, we increase the accuracy of ResNet-18 on ImageNet by 1. 31% points and ResNet-50 by 0. 90% points with equivalent model cost over previous methods.
Decoupled Model Schedule for Deep Learning Training
Specifically, the schedule works on a PyTorch model and uses a set of schedule primitives to convert the model for common model training optimizations such as high-performance kernels, effective 3D parallelism, and efficient activation checkpointing.
Binarized Neural Machine Translation
In this work, we propose a novel binarization technique for Transformers applied to machine translation (BMT), the first of its kind.
Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory
To understand whether persistent memory is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive analysis of their performance on a persistent memory device, namely, Intel Optane.
Structured Pruning is All You Need for Pruning CNNs at Initialization
In addition, since PreCropping compresses CNNs at initialization, the computational and memory costs of CNNs are reduced for both training and inference on commodity hardware.
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Using representation theory, we characterize which similarity matrices can be "expressed" by finite group VSA hypervectors, and we show how these VSAs can be constructed.
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data.
PokeBNN: A Binary Pursuit of Lightweight Accuracy
In order to enable joint optimization of the cost together with accuracy, we define arithmetic computation effort (ACE), a hardware- and energy-inspired cost metric for quantized and binarized networks.
Ranked #1 on
Binarization
on ImageNet
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Neural network robustness has become a central topic in machine learning in recent years.
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks
In this paper, we propose GARNET, a scalable spectral method to boost the adversarial robustness of GNN models for both homophilic and heterophilic graphs.
Dense Pruning of Pointwise Convolutions in the Frequency Domain
Our key insights are that 1) pointwise convolutions commute with frequency transformation and thus can be computed in the frequency domain without modification, 2) each channel within a given layer has a different level of sensitivity to frequency domain pruning, and 3) each channel's sensitivity to frequency pruning is approximately monotonic with respect to frequency.
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives.
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model.
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
We design an efficient FPGA-based accelerator for our novel BNN model that supports the fractional activations.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning
This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment.
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge.
MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators
This paper introduces MGX, a near-zero overhead memory protection scheme for hardware accelerators.
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
The proposed approach is applicable to a variety of DNN architectures and significantly reduces the computational cost of DNN execution with almost no accuracy loss.
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators
Specialized hardware for handling activation outliers can enable low-precision neural networks, but at the cost of nontrivial area overhead.
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information.
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training.
Building Efficient Deep Neural Networks with Unitary Group Convolutions
UGConvs generalize two disparate ideas in CNN architecture, channel shuffling (i. e. ShuffleNet) and block-circulant networks (i. e. CirCNN), and provide unifying insights that lead to a deeper understanding of each technique.
Channel Gating Neural Networks
Combining our method with knowledge distillation reduces the compute cost of ResNet-18 by 2. 6$\times$ without accuracy drop on ImageNet.
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.
