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05/30/2022
FBM: Fast-Bit Allocation for Mixed-Precision Quantization
Quantized neural networks are well known for reducing latency, power con...
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04/05/2022
Bimodal Distributed Binarized Neural Networks
Binary Neural Networks (BNNs) are an extremely promising method to reduc...
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01/31/2022
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
Graph isomorphism testing is usually approached via the comparison of gr...
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01/30/2022
On Recoverability of Graph Neural Network Representations
Despite their growing popularity, graph neural networks (GNNs) still hav...
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03/25/2021
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
The success of learning with noisy labels (LNL) methods relies heavily o...
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09/18/2020
FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors
Modern client processors typically use one of three commonly-used power ...
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09/08/2020
Asymmetric Aging Effect on Modern Microprocessors
Reliability is a crucial requirement in any modern microprocessor to ass...
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08/24/2020
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Unsupervised learning has always been appealing to machine learning rese...
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05/04/2020
Electromigration-Aware Architecture for Modern Microprocessors
Reliability is a fundamental requirement in any microprocessor to guaran...
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04/19/2020
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures
Convolutional Neural Networks (CNNs) have become common in many fields i...
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03/04/2020
Colored Noise Injection for Training Adversarially Robust Neural Networks
Even though deep learning have shown unmatched performance on various ta...
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11/17/2019
Smoothed Inference for Adversarially-Trained Models
Deep neural networks are known to be vulnerable to inputs with malicious...
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11/17/2019
Loss Aware Post-training Quantization
Neural network quantization enables the deployment of large models on re...
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09/25/2019
CAT: Compression-Aware Training for bandwidth reduction
Convolutional neural networks (CNNs) have become the dominant neural net...
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05/26/2019
Feature Map Transform Coding for Energy-Efficient CNN Inference
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy i...
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11/27/2018
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
Convolutional Neural Networks (CNN) has become more popular choice for v...
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09/29/2018
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Convolutional Neural Networks (CNN) are very popular in many fields incl...
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04/29/2018
UNIQ: Uniform Noise Injection for non-uniform Quantization of neural networks
We present a novel method for training a neural network amenable to infe...
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04/29/2018
UNIQ: Uniform Noise Injection for the Quantization of Neural Networks
We present a novel method for training deep neural network amenable to i...
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03/09/2018
ScaleSimulator: A Fast and Cycle-Accurate Parallel Simulator for Architectural Exploration
Design of next generation computer systems should be supported by simula...
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07/31/2017