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01/29/2023
Pipe-BD: Pipelined Parallel Blockwise Distillation
Training large deep neural network models is highly challenging due to t...
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01/25/2023
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators
Graph convolutional networks (GCNs) are becoming increasingly popular as...
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01/24/2023
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression
In training of modern large natural language processing (NLP) models, it...
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01/24/2023
Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators
Graph convolutional networks (GCNs) are becoming increasingly popular as...
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01/23/2023
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration
Co-exploration of an optimal neural architecture and its hardware accele...
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01/23/2023
SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs
Sequence alignment forms an important backbone in many sequencing applic...
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07/04/2022
ETF Portfolio Construction via Neural Network trained on Financial Statement Data
Recently, the application of advanced machine learning methods for asset...
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07/01/2022
Shai-am: A Machine Learning Platform for Investment Strategies
The finance industry has adopted machine learning (ML) as a form of quan...
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03/31/2022
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Model quantization is considered as a promising method to greatly reduce...
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11/04/2021
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Model quantization is known as a promising method to compress deep neura...
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08/18/2021
An Attention Module for Convolutional Neural Networks
Attention mechanism has been regarded as an advanced technique to captur...
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05/25/2021
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression
Data-free compression raises a new challenge because the original traini...
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02/15/2021
GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent
In this paper, we present GradPIM, a processing-in-memory architecture w...
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10/02/2020
Deep Composer Classification Using Symbolic Representation
In this study, we train deep neural networks to classify composer on a s...
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09/14/2020
DANCE: Differentiable Accelerator/Network Co-Exploration
To cope with the ever-increasing computational demand of the DNN executi...
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09/11/2020
SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks
Binary Convolutional Neural Networks (BCNNs) can significantly improve t...
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07/10/2020
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System
Generating an investment strategy using advanced deep learning methods i...
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01/14/2020
SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders
Knowing the similarity between sets of data has a number of positive imp...
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10/15/2019
MUTE: Data-Similarity Driven Multi-hot Target Encoding for Neural Network Design
Target encoding is an effective technique to deliver better performance ...
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02/28/2019
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
We applied Deep Q-Network with a Convolutional Neural Network function a...
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02/08/2018
System G Distributed Graph Database
Motivated by the need to extract knowledge and value from interconnected...
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12/25/2016