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08/23/2023
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Parameterizable machine learning (ML) accelerators are the product of re...
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06/29/2023
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
Today's performance analysis frameworks for deep learning accelerators s...
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03/06/2023
Domain-Specific Computational Storage for Serverless Computing
While (1) serverless computing is emerging as a popular form of cloud ex...
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04/07/2022
Accelerating Attention through Gradient-Based Learned Runtime Pruning
Self-attention is a key enabler of state-of-art accuracy for various tra...
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05/12/2021
Conscious AI
Recent advances in artificial intelligence (AI) have achieved human-scal...
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04/25/2020
Privacy in Deep Learning: A Survey
The ever-growing advances of deep learning in many areas including visio...
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04/11/2020
Bit-Parallel Vector Composability for Neural Acceleration
Conventional neural accelerators rely on isolated self-sufficient functi...
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03/26/2020
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference
INFerence-as-a-Service (INFaaS) in the cloud has enabled the prevalent u...
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03/04/2020
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
Recent advances demonstrate that irregularly wired neural networks from ...
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02/29/2020
Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
As deep neural networks make their ways into different domains, their co...
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01/23/2020
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
Achieving faster execution with shorter compilation time can foster furt...
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06/27/2019
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Low-power potential of mixed-signal design makes it an alluring option t...
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06/14/2019
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
The deep layers of modern neural networks extract a rather rich set of f...
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05/30/2019
Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation
Achieving faster execution with shorter compilation time can enable furt...
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05/26/2019
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge
A wide variety of DNN applications increasingly rely on the cloud to per...
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05/04/2019
SinReQ: Generalized Sinusoidal Regularization for Automatic Low-Bitwidth Deep Quantized Training
Quantization of neural networks offers significant promise in reducing t...
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11/05/2018
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
Despite numerous state-of-the-art applications of Deep Neural Networks (...
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05/10/2018
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks
Generative Adversarial Networks (GANs) are one of the most recent deep l...
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01/08/2018
In-RDBMS Hardware Acceleration of Advanced Analytics
The data revolution is fueled by advances in several areas, including da...
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12/05/2017
