tgeller08 Goto Github PK

ann-with-bayesian-output

Attaching a Bayesian Neural Network to the last layer of VGG-19 Artificial Neural Network to predict on CIFAR-10 with uncertainty estimations

bayesian-neural-network-mnist

Bayesian neural network using Pyro and PyTorch on MNIST dataset

ct-guided-biopsy-device

Code to Support a CT-Guided Biopsy Aid

digital-systems-assembly

This code contains the code that was submitted as part of EENG346: Digital Systems at Yale University taught by Professor Rajit Manohar.

following-the-footnote

Builds a database of books that are connected to each other through footnotes

large-scale-bayesian-neural-networks-on-hybrid-cmos-reram-architecture-for-energy-efficient-and-rob

This work presents an algorithm-hardware co- simulation approach to implement Bayesian inference with large- scale Convolutional Neural Networks (CNNs) on hardware in a highly resource-efficient manner. We replace the end layers of the CNN, including the output softmax, with a Bayesian Neural Network (BNN). We train this hybrid CNN+BNN model on software and deploy it on a hybrid CMOS-ReRAM based hardware architecture in an area and energy-efficient manner. Our approach introduces a novel way for deep learning algo- rithms to efficiently take advantage of Bayesian inference without the overhead of training a convolutional Bayesian model end- to-end as well as hardware complexity. We carry out analyses on VGG19 and ResNet18 networks using CIFAR10/CIFAR100 datasets and obtain > 3× energy-efficiency by deploying a hybrid VGG19+BNN architecture on hybrid CMOS-ReRAM hardware than a full-scale VGG19 BNN assumed to be implemented on a fully ReRAM-based hardware platform. Furthermore, we also find that our proposed hybrid Bayesian architecture is robust against adversarial and noisy inputs, i.e. it has the ability to identify such inputs and refuses to classify them.

m2k-fw

M2k firmware for the ADALM-2000 Active Learning Module

paradigm-shift

photocredz

A tool to help you attribute photo credits to the artist.

pytorch-vgg-cifar10

This is the PyTorch implementation of VGG network trained on CIFAR10 dataset