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08/12/2023
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
As the complexity and computational demands of deep learning models rise...
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07/07/2023
BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions
This paper presents BlendNet, a neural network architecture employing a ...
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05/08/2023
SNT: Sharpness-Minimizing Network Transformation for Fast Compression-friendly Pretraining
Model compression has become the de-facto approach for optimizing the ef...
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04/13/2023
Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks
Recent efforts to improve the performance of neural network (NN) acceler...
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03/04/2023
A Fast Training-Free Compression Framework for Vision Transformers
Token pruning has emerged as an effective solution to speed up the infer...
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07/30/2022
Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis
Recent efforts for improving the performance of neural network (NN) acce...
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04/07/2021
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic
While there is a large body of research on efficient processing of deep ...
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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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07/30/2020
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning
Machine learning models differ in terms of accuracy, computational/memor...
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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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05/10/2019
Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines
Imprecise computations provide an avenue for scheduling algorithms devel...
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12/19/2018
Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard Deadline
Energy efficiency is one of the most critical design criteria for modern...
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07/23/2018
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
Deep neural networks have been successfully deployed in a wide variety o...
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06/03/2018
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications
Major advancements in building general-purpose and customized hardware h...
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01/11/2018
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA
With ever-increasing application of machine learning models in various d...
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12/13/2017
FFT-Based Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains...
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07/06/2017