Clare Thiem

A Practical Guide to Tuning Spiking Neuronal Dynamics for Computational Neuroscience and NeuroAI Research

William Gebhardt, Nathan McDonald, Clare Thiem, Jack Lombardi et autres

xk, us (code pays fourni par la source)

A Practical Guide to Tuning Spiking Neuronal Dynamics for Computational Neuroscience and NeuroAI Research

William Gebhardt, Nathan McDonald, Clare Thiem, Jack Lombardi et autres

In this work, we examine and study the fundamental elements of spiking neural networks (SNNs) as well as how to tune them. Concretely, we focus on two different foundational neuronal units utilized in SNNs -- the leaky integrate-and-fire (LIF) and the resonate-and-fire …

Task-agnostic feature extractors for online learning at the edge

Lisa Loomis, L. David Wise, Nathan Inkawhich, Clare Thiem et autres

Machine learning (ML) at the edge typically involves pushing deep neural network (DNN) models ever closer to the sensor. In practice, a DNN deployed to a dynamic environment will quickly become obsolete if it cannot be updated to accommodate new or modified …

us (code pays fourni par la source)

The game-changing memristive technology for next-gen AI/ML hardware

Kangjun Bai, Jack P. Lombardi, Clare Thiem, Nathan McDonald

Neuromorphic computing is of high importance in Artificial Intelligence (AI) and Machine Learning (ML) to sidestep challenges inherent to neural-inspired computations in modern computing systems. Throughout the development history of neuromorphic computing, Compute-In-Memory (CIM) with emerging memory technologies, such as Resistive Random-Access …

us (code pays fourni par la source)

Temporal-encoded 6T-RRAM with Bidirectional Control for Future Neuromorphic Systems

Kangjun Bai, Hao Jiang, Zhuwei Qin, Clare Thiem

Neuromorphic systems are of high importance in artificial intelligence (AI) to eschew challenges inherent to deep learning acceleration in conventional systems. Throughout the development history of neuromorphic systems, in-memory computing with emerging memory technologies, such as resistive random-access memory (RRAM), offer advantages …

us (code pays fourni par la source)

Design principles for lifelong learning AI accelerators

Dhireesha Kudithipudi, Anurag Reddy Daram, Abdullah M. Zyarah, Fatima Tuz Zohora et autres

us, de, it (code pays fourni par la source)

Design Principles for Lifelong Learning AI Accelerators

Dhireesha Kudithipudi, Anurag Reddy Daram, Abdullah M. Zyarah, Fatima Tuz Zohora et autres

Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, …

Passive frustrated nanomagnet reservoir computing

Alexander J. Edwards, Dhritiman Bhattacharya, Peng Zhou, Nathan McDonald et autres

Abstract Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could have a transformative impact on edge computing and in-situ learning where resources are severely constrained. Ideally, a natural …

us, es (code pays fourni par la source)

Integrating complex valued hyperdimensional computing with modular artificial neural networks

Nathan McDonald, Lisa Loomis, Richard Davis, John J. Salerno et autres

Traditional approaches using Deep Neural Networks for classification, while unquestionably successful, struggle with more general intelligence tasks such as “on the fly” learning as demonstrated by biological systems. Organisms possess myriad sensory organs for interacting with their environment. By the time these …

us (code pays fourni par la source)

A journey to take technology from the lab to the field

Clare Thiem, Jack P. Lombardi, Kangjun Bai, Nathan McDonald et autres

The Air Force Research Laboratory's Information Directorate has a rich history of developing advanced computing technology for the warfighter guiding emerging technologies from the laboratory to the field. Memristors, also known as resistive random-access memory, is one such computing technology. This paper …

us (code pays fourni par la source)

Moving Towards Game-Changing Technology: Fabrication and Application of HfO2 RRAM for In-Memory Computing

Kangjun Bai, Daniel Titcombe, Jack P. Lombardi, Clare Thiem et autres

In-memory computing is an emerging computing paradigm that sidesteps challenges inherent to deep learning acceleration in conventional systems. Along with the development of neuromorphic architectures, resistive random-access memory (RRAM) has paved the way for in-memory computing by processing mixed-signal operations in a …

us (code pays fourni par la source)

Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]

Kangjun Bai, Clare Thiem, Jack P. Lombardi, Yibin Liang et autres

Reservoir computing (RC) is a neural computing paradigm especially well-suited for learning dynamical systems by leveraging an untrained reservoir layer, providing high-dimensional input encoding with fading memory property. Since only the readout weights are trained under RC, linear regression learning algorithms are …

us (code pays fourni par la source)