Adrian Wheeldon
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View article: Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs Open
Embedded Field-Programmable Gate Arrays (eFPGAs) allow for the design of hardware accelerators of edge Machine Learning (ML) applications at a lower power budget compared with traditional FPGA platforms. However, the limited eFPGA logic an…
View article: An Energy-efficient Capacitive-RRAM Content Addressable Memory
An Energy-efficient Capacitive-RRAM Content Addressable Memory Open
Content addressable memory is popular in intelligent computing systems as it allows parallel content-searching in memory. Emerging CAMs show a promising increase in bitcell density and a decrease in power consumption than pure CMOS solutio…
View article: An FPGA Architecture for Online Learning using the Tsetlin Machine
An FPGA Architecture for Online Learning using the Tsetlin Machine Open
There is a need for machine learning models to evolve in unsupervised circumstances. New classifications may be introduced, unexpected faults may occur, or the initial dataset may be small compared to the data-points presented to the syste…
View article: Energy-frugal and Interpretable AI Hardware Design using Learning Automata
Energy-frugal and Interpretable AI Hardware Design using Learning Automata Open
Energy efficiency is a crucial requirement for enabling powerful artificial intelligence applications at the microedge. Hardware acceleration with frugal architectural allocation is an effective method for reducing energy. Many emerging ap…
View article: A study on the clusterability of latent representations in image pipelines
A study on the clusterability of latent representations in image pipelines Open
Latent representations are a necessary component of cognitive artificial intelligence (AI) systems. Here, we investigate the performance of various sequential clustering algorithms on latent representations generated by autoencoder and con…
View article: A <scp>multi‐step finite‐state</scp> automaton for arbitrarily deterministic Tsetlin Machine learning
A <span>multi‐step finite‐state</span> automaton for arbitrarily deterministic Tsetlin Machine learning Open
Due to the high arithmetic complexity and scalability challenges of deep learning, there is a critical need to shift research focus towards energy efficiency. Tsetlin Machines (TMs) are a recent approach to machine learning (ML) that has d…
View article: Self-timed Reinforcement Learning using Tsetlin Machine
Self-timed Reinforcement Learning using Tsetlin Machine Open
We present a hardware design for the learning datapath of the Tsetlin machine algorithm, along with a latency analysis of the inference datapath. In order to generate a low energy hardware which is suitable for pervasive artificial intelli…
View article: Low-Power Audio Keyword Spotting Using Tsetlin Machines
Low-Power Audio Keyword Spotting Using Tsetlin Machines Open
The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current…
View article: Low-Latency Asynchronous Logic Design for Inference at the Edge
Low-Latency Asynchronous Logic Design for Inference at the Edge Open
Modern internet of things (IoT) devices leverage machine learning inference using sensed data on-device rather than offloading them to the cloud. Commonly known as inference at-the-edge, this gives many benefits to the users, including per…
View article: Low-Power Audio Keyword Spotting using Tsetlin Machines
Low-Power Audio Keyword Spotting using Tsetlin Machines Open
The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current…
View article: Learning automata based energy-efficient AI hardware design for IoT applications
Learning automata based energy-efficient AI hardware design for IoT applications Open
Energy efficiency continues to be the core design challenge for artificial intelligence (AI) hardware designers. In this paper, we propose a new AI hardware architecture targeting Internet of Things applications. The architecture is founde…
View article: A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic\n Tsetlin Machine Learning
A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic\n Tsetlin Machine Learning Open
Due to the high energy consumption and scalability challenges of deep\nlearning, there is a critical need to shift research focus towards dealing with\nenergy consumption constraints. Tsetlin Machines (TMs) are a recent approach to\nmachin…