Solving the compute crisis with physics-based ASICs
10 days ago
- #AI Hardware
- #Energy Efficiency
- #Physics-Based Computing
- Proposal of Physics-based Application-Specific Integrated Circuits (ASICs) to harness intrinsic physical dynamics for computation.
- Addresses the 'compute crisis' in AI, marked by unsustainable energy consumption, high costs, and CMOS scaling limits.
- Advocates for relaxing traditional digital constraints like statelessness, determinism, and synchronization to improve efficiency.
- Illustrates the concept with a resistor network example, showing natural optimization through physical processes.
- Introduces 'Physical Machine Learning' (PML) where hardware parameters are optimized via learning processes.
- Highlights applications in optimization, sampling, diffusion models, and scientific simulations.
- Cites existing research showing significant speedups and energy efficiency improvements with physics-based approaches.
- Outlines a three-phase roadmap for development, including proof-of-concepts, scalable substrates, and hybrid system integration.
- Calls for community collaboration to overcome challenges like analog system control and software ecosystem development.