Paper 2: Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs, led by Tousif Rahman - presents a dynamically reconfigurable Tsetlin machine inference architecture implemented on eFPGAs. Pre-print to be made available soon.

Paper 1: ETHEREAL: Energy-efficient and High-throughput Inference using Compressed Tsetlin Machine, led by Shengyu Duan. It presents an ultra low-complexity compression method for sparse Tsetlin machine inference models. Pre-print to be made available soon.

This book chapter will be part of an Edited Book to be published by Springer USA; there will be a separate announcement for this soon by the editors.

Pre-print: tinyurl.com/tinyMLChapte...

We (Omar Ghazal, Tousif Rahman and I) are really pleased to jointly author this important tutorial book chapter that introduces state-of-the-art and emerging tinyML innovation fronts to date.

The theme issue features 11 top-quality contributions. If you work on emerging technologies, machine learning accelerators and secure hardware design - the issue is totally worth browsing through and read. The issue is published TODAY. Issue link: tinyurl.com/3be5pr4d