192 citations · 731 across the 30 of their papers we have counts for
20 papers · 1 filter
UDC: Unified DNAS for Compressible TinyML Models
Igor Fedorov, Ramon Matas, Hokchhay Tann +3
Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity. Neural processing unit (NPU) hardware address the memory challenge by u…
Federated Learning Based on Dynamic Regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro +3
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen device…
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Urmish Thakker, Paul N. Whatmough, Zhigang Liu +2
Structured matrices, such as those derived from Kronecker products (KP), are effective at compressing neural networks, but can lead to unacceptable accuracy loss when applied to la…
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Martin Ferianc, Partha Maji, Matthew Mattina +1
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quanti…
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Dibakar Gope, Jesse Beu, Matthew Mattina
Matrix multiplications between asymmetric bit-width operands, especially between 8- and 4-bit operands are likely to become a fundamental kernel of many important workloads includi…
Efficient Residue Number System Based Winograd Convolution
Zhi-Gang Liu, Matthew Mattina
Prior research has shown that Winograd algorithm can reduce the computational complexity of convolutional neural networks (CNN) with weights and activations represented in floating…
Searching for Winograd-aware Quantized Networks
Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy +1
Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications…
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation
Chuteng Zhou, Prad Kadambi, Matthew Mattina +1
The success of deep learning has brought forth a wave of interest in computer hardware design to better meet the high demands of neural network inference. In particular, analog com…
Compressing Language Models using Doped Kronecker Products
Urmish Thakker, Paul N. Whatmough, Zhi-Gang Liu +2
Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when K…
Ternary MobileNets via Per-Layer Hybrid Filter Banks
Dibakar Gope, Jesse Beu, Urmish Thakker +1
MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applica…
Pushing the limits of RNN Compression
Urmish Thakker, Igor Fedorov, Jesse Beu +4
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can signi…
Run-Time Efficient RNN Compression for Inference on Edge Devices
Urmish Thakker, Jesse Beu, Dibakar Gope +2
Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities w…
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Urmish Thakker, Jesse Beu, Dibakar Gope +4
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size.As a result, there is a need for compression techniques that can signif…
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Igor Fedorov, Ryan P. Adams, Matthew Mattina +1
The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from autom…
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
Dibakar Gope, Ganesh Dasika, Matthew Mattina
Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neu…
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
Partha Maji, Andrew Mundy, Ganesh Dasika +3
The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs). Although there has been a lot…
Learning low-precision neural networks without Straight-Through Estimator(STE)
Zhi-Gang Liu, Matthew Mattina
The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understandi…
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning
Paul Whatmough, Chuteng Zhou, Patrick Hansen +1
On-device CNN inference for real-time computer vision applications can result in computational demands that far exceed the energy budgets of mobile devices. This paper proposes Fix…
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective
Yuhao Zhu, Matthew Mattina, Paul Whatmough
Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customi…