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09/13/2023
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
While diffusion models demonstrate a remarkable capability for generatin...
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06/08/2023
Island-based Random Dynamic Voltage Scaling vs ML-Enhanced Power Side-Channel Attacks
In this paper, we describe and analyze an island-based random dynamic vo...
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04/28/2023
Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP
Logic locking has become a promising approach to provide hardware securi...
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12/20/2022
Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Spiking Neural networks (SNN) have emerged as an attractive spatio-tempo...
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10/23/2022
Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMs
Spiking Neural Networks (SNNs) have emerged as an attractive spatio-temp...
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10/11/2022
Enabling ISP-less Low-Power Computer Vision
In order to deploy current computer vision (CV) models on resource-const...
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03/11/2022
Toward Efficient Hyperspectral Image Processing inside Camera Pixels
Hyperspectral cameras generate a large amount of data due to the presenc...
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01/16/2022
TriLock: IC Protection with Tunable Corruptibility and Resilience to SAT and Removal Attacks
Sequential logic locking has been studied over the last decade as a meth...
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12/22/2021
Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?
Spiking neural networks (SNNs), that operate via binary spikes distribut...
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08/10/2021
Fun-SAT: Functional Corruptibility-Guided SAT-Based Attack on Sequential Logic Encryption
The SAT attack has shown to be efficient against most combinational logi...
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01/28/2021
GF-Flush: A GF(2) Algebraic Attack on Secure Scan Chains
Scan chains provide increased controllability and observability for test...
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11/03/2020
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs
This paper presents a dynamic network rewiring (DNR) method to generate ...
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04/06/2020
Formal Verification of Flow Equivalence in Desynchronized Designs
Seminal work by Cortadella, Kondratyev, Lavagno, and Sotiriou includes a...
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03/27/2020
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
We present Deep-n-Cheap – an open-source AutoML framework to search for ...
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01/29/2020
qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit
Single flux quantum (SFQ) circuits are an attractive beyond-CMOS technol...
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01/29/2020
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks
The high energy cost of processing deep convolutional neural networks im...
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01/13/2020
SERAD: Soft Error Resilient Asynchronous Design using a Bundled Data Protocol
The risk of soft errors due to radiation continues to be a significant c...
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10/22/2019
Neural Network Training with Approximate Logarithmic Computations
The high computational complexity associated with training deep neural n...
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10/02/2019
A Pre-defined Sparse Kernel Based Convolution for Deep CNNs
The high demand for computational and storage resources severely impede ...
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10/02/2019
A Pre-defined Sparse Kernel Based Convolutionfor Deep CNNs
The high demand for computational and storage resources severely impede ...
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06/25/2019
Automatic Conversion from Flip-flop to 3-phase Latch-based Designs
Latch-based designs have many benefits over their flip-flop based counte...
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12/04/2018
Pre-Defined Sparse Neural Networks with Hardware Acceleration
Neural networks have proven to be extremely powerful tools for modern ar...
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07/11/2018
Morse Code Datasets for Machine Learning
We present an algorithm to generate synthetic datasets of tunable diffic...
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05/31/2018
A Highly Parallel FPGA Implementation of Sparse Neural Network Training
We demonstrate an FPGA implementation of a parallel and reconfigurable a...
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11/06/2017
Characterizing Sparse Connectivity Patterns in Neural Networks
We propose a novel way of reducing the number of parameters in the stora...
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11/03/2017