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01/29/2023
Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher
The irregular nature of memory accesses of graph workloads makes their p...
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11/26/2022
Profile-Guided Parallel Task Extraction and Execution for Domain Specific Heterogeneous SoC
In this study, we introduce a methodology for automatically transforming...
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11/17/2022
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
Line-of-sight link blockages represent a key challenge for the reliabili...
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08/18/2022
An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
Sampling is an essential part of raw point cloud data processing such as...
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05/09/2022
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
This work aims to tackle Model Inversion (MI) attack on Split Federated ...
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11/18/2021
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
Line-of-sight link blockages represent a key challenge for the reliabili...
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11/16/2021
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
Overcoming the link blockage challenges is essential for enhancing the r...
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08/14/2021
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
In-memory computing (IMC) on a monolithic chip for deep learning faces d...
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08/01/2021
Versa: A Dataflow-Centric Multiprocessor with 36 Systolic ARM Cortex-M4F Cores and a Reconfigurable Crossbar-Memory Hierarchy in 28nm
We present Versa, an energy-efficient processor with 36 systolic ARM Cor...
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07/20/2021
NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing
Neural network stealing attacks have posed grave threats to neural netwo...
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07/20/2021
Communication and Computation Reduction for Split Learning using Asynchronous Training
Split learning is a promising privacy-preserving distributed learning sc...
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07/06/2021
Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks
With the widespread use of Deep Neural Networks (DNNs), machine learning...
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01/20/2021
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
Adversarial attacks on Neural Network weights, such as the progressive b...
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01/18/2021
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
Millimeter wave (mmWave) communication is a key component of 5G and beyo...
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07/24/2020
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Deep Neural Network (DNN) attacks have mostly been conducted through adv...
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01/27/2020
Automated Parallel Kernel Extraction from Dynamic Application Traces
Modern program runtime is dominated by segments of repeating code called...
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04/19/2018
Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression
Deep learning algorithms have shown tremendous success in many recogniti...
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09/19/2017