low-power

Details a method to run a 13 million parameter ASR Conformer model directly on a microcontroller, highlighting advances in edge AI deployment.

This study explores the feasibility of classifying ten hand gestures using a single-channel sEMG signal combined with lightweight machine learning models, achieving up to 90% accuracy. It demonstrates potential for cost-effective, low-power gesture recognition.

A new brain-inspired hardware design enables faster and more energy-efficient anomaly detection for AI systems, drawing on principles of neuromorphic computing.

A fully biocompatible multiterminal neuromorphic biodevice using quaternized chitosan is developed, operating at ultralow voltages and simulating visual nervous system processing.

Texas Instruments introduced the MSPM0C1104 microcontroller, a 1.38 mm² device featuring an ARM Cortex-M0+ CPU at 24MHz with 16KB flash and 1KB SRAM, designed for small form factor and low-power embedded applications.

This paper presents low-power analogue neural networks that place trainable nonlinear functions on connections, inspired by Kolmogorov-Arnold networks, enabling efficient continuous control tasks with far fewer nodes and connections than multilayer perceptrons, demonstrated on hardware with projected microWatt power.

MIT researchers have developed a new system-on-a-chip that enables tiny robots to create detailed 3D maps of their environments in real-time using only about 6 milliwatts of power, potentially enabling long-duration autonomous navigation in complex spaces.

A developer ran DeepSeek-V4-Flash on a Raspberry Pi 5 by streaming model weights from an NVMe SSD, achieving 1.3 tokens/second at 8 watts, demonstrating the feasibility of frontier-adjacent open-weight models on low-cost, offline hardware.

A developer successfully ran the 284B-parameter DeepSeek-V4-Flash model on a Raspberry Pi 5 at over 1 tok/s, using an untouched GGUF file from antirez after extensive experimentation.