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09/05/2023
Using a Nearest-Neighbour, BERT-Based Approach for Scalable Clone Detection
Code clones can detrimentally impact software maintenance and manually d...
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03/27/2023
Maple: A Processing Element for Row-Wise Product Based Sparse Tensor Accelerators
Sparse tensor computing is a core computational part of numerous applica...
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02/21/2023
Dynamic Resource Partitioning for Multi-Tenant Systolic Array Based DNN Accelerator
Deep neural networks (DNN) have become significant applications in both ...
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11/07/2022
LOCAL: Low-Complex Mapping Algorithm for Spatial DNN Accelerators
Deep neural networks are a promising solution for applications that solv...
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05/04/2022
Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information
Channel pruning is used to reduce the number of weights in a Convolution...
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01/27/2022
On the RTL Implementation of FINN Matrix Vector Compute Unit
FPGA-based accelerators are becoming more popular for deep neural networ...
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01/25/2022
Winograd Convolution for Deep Neural Networks: Efficient Point Selection
Convolutional neural networks (CNNs) have dramatically improved the accu...
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02/12/2021
Low precision logarithmic number systems: Beyond base-2
Logarithmic number systems (LNS) are used to represent real numbers in m...
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07/11/2020
HOBFLOPS CNNs: Hardware Optimized Bitsliced Floating-Point Operations Convolutional Neural Networks
Convolutional neural network (CNN) inference is commonly performed with ...
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06/22/2020
Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization
Pruning and quantization are proven methods for improving the performanc...
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05/21/2020
TASO: Time and Space Optimization for Memory-Constrained DNN Inference
Convolutional neural networks (CNNs) are used in many embedded applicati...
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04/03/2020
Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle
The computation and memory needed for Convolutional Neural Network (CNN)...
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01/09/2020
Performance-Oriented Neural Architecture Search
Hardware-Software Co-Design is a highly successful strategy for improvin...
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06/11/2019
A Taxonomy of Channel Pruning Signals in CNNs
Convolutional neural networks (CNNs) are widely used for classification ...
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05/13/2019
Winograd Convolution for DNNs: Beyond linear polinomials
We investigated a wider range of Winograd family convolution algorithms ...
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09/27/2018
Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks
Quantization of weights and activations in Deep Neural Networks (DNNs) i...
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03/29/2018
Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks
Modern deep neural networks (DNNs) spend a large amount of their executi...
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03/29/2018
Improving accuracy of Winograd convolution for DNNs
Modern deep neural networks (DNNs) spend a large amount of their executi...
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01/30/2018
Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing
Convolutional neural networks (CNNs) are one of the most successful mach...
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10/03/2017
Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming
Deep Neural Networks (DNNs) require very large amounts of computation bo...
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09/08/2017
Low-memory GEMM-based convolution algorithms for deep neural networks
Deep neural networks (DNNs) require very large amounts of computation bo...
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04/06/2017
Parallel Multi Channel Convolution using General Matrix Multiplication
Convolutional neural networks (CNNs) have emerged as one of the most suc...
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11/16/2016
Spectral Convolution Networks
Previous research has shown that computation of convolution in the frequ...
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08/30/2016
Low Complexity Multiply Accumulate Unit for Weight-Sharing Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are one of the most successful deep...
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02/15/2016
Customizable Precision of Floating-Point Arithmetic with Bitslice Vector Types
Customizing the precision of data can provide attractive trade-offs betw...
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01/26/2016