Sparsity in coupled oscillator networks cuts up to 98.4% of connections in our Un-0 image generation model while improving performance by 1.9 FID on ImageNet 64x64.
The universal memory never arrived. Here's how relaxing error rates lets each memory technology, from PCM to eDRAM, finally find its job in the age of AI.
DS-ISA, the first Instruction Set Architecture for dynamical system hardware: a new vocabulary for software to program AI that computes with physics.
Exploring digital twins, chaos theory, numerical simulation, and system modeling as the foundation for neural co-evolution at Unconventional AI.
A technical exploration of machine learning with dynamics using gyroscopes, springs, coupled nonlinear ODEs, and differentiable physical systems for sequence classification.
Why memory, not compute, is the real bottleneck in AI energy efficiency. A deep dive into data movement, Amdahl’s Law, and the path to 1000x inference efficiency.
As AI systems hit energy efficiency limits, this post explores the tradeoffs between analog and digital computing and why future AI may require a mixed-signal approach.
For 60 years, hardware and software have been siloed. Discover how Unconventional AI is breaking these barriers through "neural co-evolution"—co-designing neural networks and physical systems to unlock 1000x efficiency.
