Conference Presentation, Interview
NextSilicon & More Than Moore. The Silicon Revolution: Why the Future of Hardware is Software
- The company anticipates a 4x to 10x performance speedup with half or quarter of the power consumption, contrasting with ideal scenarios where specific kernels could run 100 times faster, while avoiding reliance on brute-force scaling like two-nanometer chips or fixed-function units.
- Development strategy prioritizes the 1% of parallel software code responsible for the majority of execution time using a dynamic software algorithm, rejecting the creation of domain-specific languages or custom chips for this segment, in alignment with the Pareto principle.
- A next-generation compute platform will be designed to simultaneously execute CPU, GPU, AI, and machine learning workloads, featuring the ability to reconfigure the chip in approximately 20 nanoseconds to launch new kernels based on real-time learning without requiring multiple execution runs.
- The architecture ensures backward compatibility by supporting existing software stacks including C, C++, Fortran, Rust, CUDA, and APIs as drop-in replacements, aiming to eliminate the need for users to rewrite code or build custom software ecosystems comparable to the billions spent by competitors.
- The dynamic runtime approach is positioned to overcome limitations of static compilers like Itanium and the constraints of Amdahl's Law by utilizing real-time telemetry to intelligently schedule tasks, handle "stop and fence" issues between serial and parallel execution, and adapt to varying activation functions like GeLU or Swish.
- Market analysis predicts industry consolidation where rigid hardware designs will fail to adapt to architectural shifts like the move to transformers, while the company expects to compete against giants like Intel, NVIDIA, and TSMC by outmaneuvering their $10 billion investment strategies with runtime learning capabilities.
- Significant challenges are expected regarding developer reluctance to abandon highly optimized CUDA stacks, though the solution aims to run VLLMs with custom kernels "just out of the box" by addressing the "blood, sweat, and tears" invested in previous GPU efficiency efforts.
- Long-term industry trends indicate that frequency scaling is no longer efficient due to power consumption constraints comparable to rocket nozzles, necessitating a shift from serial to parallel architectures and back to serial schemas for specific code segments within the new dynamic framework.