3 articles tagged with #compute-in-memory. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed an SRAM-based compute-in-memory accelerator for spiking neural networks that uses linear decay approximation instead of exponential decay, achieving 1.1x to 16.7x reduction in energy consumption. The innovation addresses the bottleneck of neuron state updates in neuromorphic computing by performing in-place decay directly within memory arrays.
Researchers outline how neuromorphic computing could overcome energy efficiency limits in classical CMOS technology for AI applications. The approach requires co-design across materials, circuits, and algorithms to achieve brain-inspired compute-in-memory architectures.
Researchers present a unified framework for probabilistic AI computation that treats deterministic and stochastic data access under a common perspective. The study identifies memory systems as performance bottlenecks in trustworthy AI and proposes compute-in-memory approaches to address scalability challenges.
