🤖AI Summary
Researchers at UC San Diego developed a new type of bulk resistive RAM (RRAM) that overcomes traditional limitations by switching entire layers rather than forming filaments. The technology achieved 90% accuracy in AI learning tasks and could enable more efficient edge computing by allowing computation within memory itself.
Key Takeaways
- →New bulk RRAM design eliminates unstable filaments, switching entire layers for better reliability and integration
- →UCSD researchers created 40-nanometer devices stacked in 8 layers with 64 resistance values each
- →The technology achieved 90% accuracy in continual learning algorithms for wearable sensor data classification
- →Bulk RRAM operates in megaohm range enabling better parallel operations without selector transistors
- →Data retention at high operating temperatures remains a key challenge for practical deployment
#rram#memory-wall#ai-hardware#neural-networks#edge-computing#3d-stacking#ucsd-research#continual-learning
Read Original →via IEEE Spectrum – AI
Act on this with AI
Stay ahead of the market.
Connect your wallet to an AI agent. It reads balances, proposes swaps and bridges across 15 chains — you keep full control of your keys.
Related Articles