151 citations · 437 across the 32 of their papers we have counts for
8 papers · 1 filter
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…
A Roadmap for Enabling a Future-Proof In-Network Computing Data Plane Ecosystem
Daehyeok Kim, Nikita Lazarev, Tommy Tracy +7
As the vision of in-network computing becomes more mature, we see two parallel evolutionary trends. First, we see the evolution of richer, more demanding applications that require…
Dense Pruning of Pointwise Convolutions in the Frequency Domain
Mark Buckler, Neil Adit, Yuwei Hu +2
Depthwise separable convolutions and frequency-domain convolutions are two recent ideas for building efficient convolutional neural networks. They are seemingly incompatible: the v…
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…
Scaling Up Hardware Accelerator Verification using A-QED with Functional Decomposition
Saranyu Chattopadhyay, Florian Lonsing, Luca Piccolboni +12
Hardware accelerators (HAs) are essential building blocks for fast and energy-efficient computing systems. Accelerator Quick Error Detection (A-QED) is a recent formal technique wh…
Dagger: Accelerating RPCs in Cloud Microservices Through Tightly-Coupled Reconfigurable NICs
Nikita Lazarev, Shaojie Xiang, Neil Adit +2
The ongoing shift of cloud services from monolithic designs to microservices creates high demand for efficient and high performance datacenter networking stacks, optimized for fine…
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Cong Hao, Jordan Dotzel, Jinjun Xiong +3
Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workload…
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…