IEEE

On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding

Abstract: Artificial Neural Networks (ANNs) have shown remarkable performance in various fields. However, ANN relies on the von-Neumann architecture, which consumes a lot of power. Hardware-based ...
Electronic Design

A Neural-Network-Based Approach to Smarter DPD Engines

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.
GitHub

ANN Visualizer

A great visualization python library used to work with Keras. It uses python's graphviz library to create a presentable graph of the neural network you are building. This library is still unstable.
GitHub

Neural-Symbolic Visual Question Answering (NS-VQA)

In Neural Information Processing Systems (NeurIPS) 2018. @inproceedings{yi2018neural, title={Neural-symbolic vqa: Disentangling reasoning from vision and language understanding}, author={Yi, Kexin and ...
IEEE

End-to-End Design Flow for Resistive Neural Accelerators

Abstract: Neural hardware accelerators have demonstrated notable energy efficiency in tackling tasks, which can be adapted to artificial neural network (ANN) structures. Research is currently directed ...
Frontiers

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial ...
InfoQ

Building Neural Networks with TensorFlow.NET

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow, allowing ...