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01/15/2022
UDC: Unified DNAS for Compressible TinyML Models
Emerging Internet-of-things (IoT) applications are driving deployment of...
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08/13/2021
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
In recent years graph neural network (GNN)-based approaches have become ...
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07/16/2021
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration
Exploiting sparsity is a key technique in accelerating quantized convolu...
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02/22/2021
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Bayesian neural networks (BNNs) are making significant progress in many ...
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02/14/2021
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Structured matrices, such as those derived from Kronecker products (KP),...
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01/28/2021
Information contraction in noisy binary neural networks and its implications
Neural networks have gained importance as the machine learning models th...
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10/21/2020
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
Executing machine learning workloads locally on resource constrained mic...
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10/06/2020
Rank and run-time aware compression of NLP Applications
Sequence model based NLP applications can be large. Yet, many applicatio...
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09/04/2020
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration
Convolutional neural network (CNN) inference on mobile devices demands e...
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08/03/2020
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Matrix multiplications between asymmetric bit-width operands, especially...
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07/23/2020
Efficient Residue Number System Based Winograd Convolution
Prior research has shown that Winograd algorithm can reduce the computat...
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05/20/2020
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Modern speech enhancement algorithms achieve remarkable noise suppressio...
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05/16/2020
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference
Convolutional neural network (CNN) inference on mobile devices demands e...
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02/25/2020
Searching for Winograd-aware Quantized Networks
Lightweight architectural designs of Convolutional Neural Networks (CNNs...
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01/24/2020
Compressing Language Models using Doped Kronecker Products
Kronecker Products (KP) have been used to compress IoT RNN Applications ...
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01/14/2020
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation
The success of deep learning has brought forth a wave of interest in com...
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11/18/2019
ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems
Convolutional neural networks (CNNs) are now predominant components in a...
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11/04/2019
Ternary MobileNets via Per-Layer Hybrid Filter Banks
MobileNets family of computer vision neural networks have fueled tremend...
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10/04/2019
Pushing the limits of RNN Compression
Recurrent Neural Networks (RNN) can be difficult to deploy on resource c...
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06/12/2019
Run-Time Efficient RNN Compression for Inference on Edge Devices
Recurrent neural networks can be large and compute-intensive, yet many a...
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06/07/2019
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Recurrent Neural Networks (RNN) can be large and compute-intensive, maki...
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05/28/2019
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
The vast majority of processors in the world are actually microcontrolle...
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04/05/2019
Measuring scheduling efficiency of RNNs for NLP applications
Recurrent neural networks (RNNs) have shown state of the art results for...
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03/04/2019
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
Machine learning-based applications are increasingly prevalent in IoT de...
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03/04/2019
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
The Winograd or Cook-Toom class of algorithms help to reduce the overall...
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03/04/2019
Learning low-precision neural networks without Straight-Through Estimator(STE)
The Straight-Through Estimator (STE) is widely used for back-propagating...
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02/27/2019
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
The computational demands of computer vision tasks based on state-of-the...
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12/05/2018
Efficient and Robust Machine Learning for Real-World Systems
While machine learning is traditionally a resource intensive task, embed...
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12/04/2018
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning
On-device CNN inference for real-time computer vision applications can r...
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10/16/2018
SCALE-Sim: Systolic CNN Accelerator Simulator
Systolic Arrays are one of the most popular compute substrates within De...
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10/16/2018
SCALE-Sim: Systolic CNN Accelerator
Systolic Arrays are one of the most popular compute substrates within De...
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03/29/2018
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision
Continuous computer vision (CV) tasks increasingly rely on convolutional...
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01/19/2018