AITowards Data Scienceabout 11 hours ago
Analog AI Returns With Noise Challenges
1 min read
Analog in-memory computing uses physics for matrix multiplies in one step, avoiding the von Neumann bottleneck. IBM's HERMES chip and startups EnCharge AI and Mythic pursue this approach. Simulations show over 8% error from noise on a single layer, with accuracy dropping to 64% at higher noise levels.
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