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Jim Keller: Abstraction Layers from the Atom to the Data Center | AI Podcast Clips
- x86 and ARM instruction sets will remain stable for extended periods, ensuring continued viability of Intel architecture based on its foundational design.
- The market for simple, slow computers is currently non-existent, driving demand for systems that execute vast instruction counts and compute dependency graphs to optimize parallelism.
- Modern processors will operate deeply out of order to achieve performance ratios of 0.2 to 0.25 cycles per instruction, unlocking 10x parallelism compared to in-order execution.
- Branch prediction mechanisms will aim for high 90% accuracy to support fetch windows of 500 to 600 instructions, with systems targeting 99% accuracy requiring tens of megabits of logic, representing a 3 to 4 order-of-magnitude increase from 1,000-bit predecessors.
- Advanced pattern recognition resembling neural networks will be employed to predict branch directions by analyzing all execution flows, while systems may retain results from mispredicted paths to enable value reuse upon correction.
- Achieving future computer design goals will demand teams combining intuitive leaps with rigorous alternative evaluation, driven by an approach characterized as 99% perspiration and 1% inspiration.
- While most programming users expect deterministic results where every run yields identical outputs for correct programs, future AI algorithms may intentionally incorporate noise to accelerate convergence in noisy data environments.
- Systems are expected to deliver deterministic answers despite internal execution flows that are non-deterministic and do not repeat the same path twice.
- Some GPU calculations utilizing low-precision data may still produce varying results across runs, a phenomenon generally considered undesirable for High-Performance Computing (HPC).
- Developers will likely continue to desire a switch or mechanism to enforce deterministic behavior that overrides internal system noise.