#in-memory-computing

LIVE PULSE

5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src

2 curated events

papersSEP 12 04:00 UTC

Time-Based Readout Method for Analog Memristive Spiking Neural Networks

A new arXiv paper proposes a time-based readout scheme for vector-matrix multiplication in fully analog memristive spiking neural networks. Standard digital hardware performs these operations inefficiently because moving data between memory and processing units dominates cost and energy use. The approach aims to keep computation in the analog memory array, addressing the data-movement bottleneck that spiking networks are meant to reduce.

memristors/spiking-neural-networks/hardware/in-memory-computing

papersSEP 12 04:00 UTC

Paper Explores Probabilistic In-Memory Hardware for Bayesian Learning

A new arXiv preprint examines how the neural dynamics behind Bayesian learning and decision-making in animals could be recreated in hardware. The authors propose using probabilistic in-memory computing circuits to integrate sensory evidence with prior beliefs under uncertainty. The work is presented as the first part of a series linking bio-inspired computation to physical device design.

hardware/neuromorphic/bayesian/in-memory-computing