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08/03/2023
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators
Progress in artificial intelligence and machine learning over the past d...
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03/13/2023
X-Former: In-Memory Acceleration of Transformers
Transformers have achieved great success in a wide variety of natural la...
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10/02/2022
Approximate Computing and the Efficient Machine Learning Expedition
Approximate computing (AxC) has been long accepted as a design alternati...
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06/03/2022
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Deep Learning neural networks are pervasive, but traditional computer ar...
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03/30/2022
STeP-CiM: Strain-enabled Ternary Precision Computation-in-Memory based on Non-Volatile 2D Piezoelectric Transistors
We propose 2D Piezoelectric FET (PeFET) based compute-enabled non-volati...
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02/28/2022
Piezoelectric Strain FET (PeFET) based Non-Volatile Memories
We propose non-volatile memory (NVM) designs based on Piezoelectric Stra...
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05/08/2021
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Deep Neural Networks (DNNs) have transformed the field of machine learni...
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03/19/2021
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks
Graph Neural Networks (GNNs) use a fully-connected layer to extract feat...
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03/16/2021
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Transformers have transformed the field of natural language processing. ...
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02/28/2021
HW/SW Framework for Improving the Safety of Implantable and Wearable Medical Devices
Implantable and wearable medical devices (IWMDs) are widely used for the...
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11/25/2020
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration
Precision scaling has emerged as a popular technique to optimize the com...
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10/07/2020
Optimizing Transformers with Approximate Computing for Faster, Smaller and more Accurate NLP Models
Transformer models have garnered a lot of interest in recent years by de...
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06/14/2020
Adversarial Sparsity Attacks on Deep Neural Networks
Adversarial attacks have exposed serious vulnerabilities in Deep Neural ...
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04/21/2020
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their a...
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03/05/2020
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Modern deep networks have millions to billions of parameters, which lead...
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02/25/2020
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
Resistive crossbars have attracted significant interest in the design of...
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02/23/2020
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks
The enormous inference cost of deep neural networks can be scaled down b...
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09/15/2019
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
The use of lower precision has emerged as a popular technique to optimiz...
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09/05/2018
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
Object detection in videos is an important task in computer vision for v...
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08/31/2018
Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars
Deep Neural Networks (DNNs) are widely used to perform machine learning ...
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11/07/2017
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
Deep Neural Networks (DNNs) have emerged as the method of choice for sol...
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04/04/2017
DyVEDeep: Dynamic Variable Effort Deep Neural Networks
Deep Neural Networks (DNNs) have advanced the state-of-the-art in a vari...
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02/27/2016
Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing
Large-scale artificial neural networks have shown significant promise in...
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09/28/2015
