Energy Efficient and high throughput inference using compressed tsetlin machine

EDGE AI POD

Logic beats arithmetic in the machine learning revolution happening at Newcastle University. From a forgotten Soviet mathematician's work in the 1960s to modern embedded systems, Settle Machine represents a paradigm shift in how we approach artificial intelligence.
Unlike traditional neural networks that rely on complex mathematical operations, Settle Machine harnesses Boolean logic - simple yes/no questions similar to how humans naturally think. This "white box" approach creates interpretable models using only AND gates, OR gates, and NOT gates without any multiplication operations. The result? Machine learning that's not only understandable but dramatically more efficient.
The technical magic happens through a process called Booleanization, converting input data into binary questions that feed learning automata. These finite state machines work in parallel, creating logical patterns that combine to make decisions. What's remarkable is the natural sparsity of the resulting models - for complex tasks like image recognition, more than 99% of potential features are automatically excluded. By further optimizing this sparsity and removing "weak includes," Newcastle's team has achieved astonishing efficiency improvements.
The numbers don't lie: 10x faster inference time than Binarized Neural Networks, dramatically lower memory footprint, and energy efficiency improvements around 20x on embedded platforms. Their latest microchip implementation consumes just 8 nanojoules per frame for MNIST character recognition - likely the lowest energy consumption ever published for this benchmark. For edge computing and IoT applications where power constraints are critical, this breakthrough opens new possibilities.
Beyond efficiency, Settle Machine addresses the growing demand for explainable AI. As regulations tighten around automated decision-making, the clear logical propositions generated by this approach provide transparency that black-box neural networks simply can't match. Ready to explore this revolutionary approach? Visit settlemachine.org or search for the unified GitHub repository to get started with open-source implementations.

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1. Introduction to Newcastle's ML Research (00:00:00)

2. Origins and Principles of Settle Machine (00:02:20)

3. White Box Logic-Based Machine Learning Approach (00:04:55)

4. Booleanization and Learning Automata Process (00:06:26)

5. Exploiting Sparsity in Settle Machine (00:09:57)

6. Performance Comparison with Other ML Systems (00:14:30)

7. Results and Energy Efficiency Improvements (00:19:30)