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The Bonsai 1.7B AI model can solve simple physics problems efficiently on a low-power Intel N97 processor, achieving inference speeds of about 9.1 tokens per second.
Savartus's Enterprise Laser Storage offers physically immutable optical archive libraries for secure, long-term data preservation, featuring EMP-proof design, 50-100 year media life, and low power consumption.
M5Stack has launched PaperMono, a compact E-Ink development terminal powered by ESP32-S3, designed for low-power IoT projects with features like touch display, NFC, and LoRa connectivity.
Pants for Birds introduces the ADSBee m1421, the world's smallest dual-band ADS-B receiver module, an open-source, low-power hardware designed for embedded aircraft tracking applications.
The article describes the construction of a portable, sensitive, and low-power analog Geiger counter, designed for measuring low radiation levels using a large Geiger-Müller tube.
A hobbyist describes building a low-power llama.cpp server using an Intel N100 motherboard and a refurbished RTX 5060 Ti, sharing performance numbers, power consumption, and model choices.
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.