research
∙
08/11/2023
Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures
Non-volatile memory (NVM) crossbars have been identified as a promising ...
share
research
∙
08/05/2023
Towards Fast, Adaptive, and Hardware-Assisted User-Space Scheduling
Modern datacenter applications are prone to high tail latencies since th...
research
∙
02/16/2023
Decoupled Model Schedule for Deep Learning Training
Recent years have seen an increase in the development of large deep lear...
research
∙
02/09/2023
Binarized Neural Machine Translation
The rapid scaling of language models is motivating research using low-bi...
research
∙
09/06/2022
TAPA: A Scalable Task-Parallel Dataflow Programming Framework for Modern FPGAs with Co-Optimization of HLS and Physical Design
In this paper, we propose TAPA, an end-to-end framework that compiles a ...
research
∙
07/25/2022
Benchmarking GNN-Based Recommender Systems on Intel Optane Persistent Memory
Graph neural networks (GNNs), which have emerged as an effective method ...
research
∙
03/04/2022
Structured Pruning is All You Need for Pruning CNNs at Initialization
Pruning is a popular technique for reducing the model size and computati...
research
∙
02/10/2022
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Hyperdimensional computing (HDC) is an emerging learning paradigm that c...
research
∙
01/30/2022
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Graph neural networks (GNNs) have been increasingly deployed in various ...
research
∙
11/30/2021
PokeBNN: A Binary Pursuit of Lightweight Accuracy
Top-1 ImageNet optimization promotes enormous networks that may be impra...
research
∙
11/08/2021
A Roadmap for Enabling a Future-Proof In-Network Computing Data Plane Ecosystem
As the vision of in-network computing becomes more mature, we see two pa...
research
∙
09/29/2021
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Neural network robustness has become a central topic in machine learning...
research
∙
09/16/2021
Dense Pruning of Pointwise Convolutions in the Frequency Domain
Depthwise separable convolutions and frequency-domain convolutions are t...
research
∙
06/02/2021
Dagger: Accelerating RPCs in Cloud Microservices Through Tightly-Coupled Reconfigurable NICs
The ongoing shift of cloud services from monolithic designs to microserv...
research
∙
03/25/2021
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Artificial intelligence (AI) technologies have dramatically advanced in ...
research
∙
02/07/2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
A black-box spectral method is introduced for evaluating the adversarial...
research
∙
12/22/2020
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
Binary neural networks (BNNs) have 1-bit weights and activations. Such n...
research
∙
08/26/2020
GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning
This paper proposes GuardNN, a secure deep neural network (DNN) accelera...
research
∙
08/26/2020
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Graph neural networks (GNNs) are gaining increasing popularity as a prom...
research
∙
07/16/2020
Dagger: Towards Efficient RPCs in Cloud Microservices with Near-Memory Reconfigurable NICs
Cloud applications are increasingly relying on hundreds of loosely-coupl...
research
∙
04/20/2020
MgX: Near-Zero Overhead Memory Protection with an Application to Secure DNN Acceleration
In this paper, we propose MgX, a near-zero overhead memory protection sc...
research
∙
04/09/2020
Predictable Accelerator Design with Time-Sensitive Affine Types
Field-programmable gate arrays (FPGAs) provide an opportunity to co-desi...
research
∙
02/17/2020
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
We propose precision gating (PG), an end-to-end trainable dynamic dual-p...
research
∙
10/13/2019
Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators
Outliers in weights and activations pose a key challenge for fixed-point...
research
∙
10/06/2019
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
Graph embedding techniques have been increasingly deployed in a multitud...
research
∙
04/15/2019
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Physical design process commonly consumes hours to days for large design...
research
∙
01/28/2019
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Quantization can improve the execution latency and energy efficiency of ...
research
∙
01/28/2019
Improving Neural Network Quantization using Outlier Channel Splitting
Quantization can improve the execution latency and energy efficiency of ...
research
∙
11/19/2018
Building Efficient Deep Neural Networks with Unitary Group Convolutions
We propose unitary group convolutions (UGConvs), a building block for CN...
research
∙
05/29/2018
Channel Gating Neural Networks
Employing deep neural networks to obtain state-of-the-art performance on...
research
∙
07/15/2017