Moving past speculation: How deterministic CPUs deliver predictable AI performance

Modern CPUs use speculative execution to improve performance, but it introduces vulnerabilities and energy waste. A novel deterministic, time-based execution model offers an alternative by assigning each instruction a precise execution slot. This approach utilizes a time counter to schedule instructions based on dependencies and resource availability. It eliminates guesswork and replaces it with predictable execution flow which challenges existing speculative approaches. The architecture extends to matrix computation and shows scalability rivaling Google's TPUs with lower costs. This method acknowledges latency, but fills it with useful work, avoiding rollbacks and speculative comparators. Speculation increases unpredictability and power inefficiency, especially with AI and ML workloads, causing performance cliffs and security exploits. This time-based execution model uses a vector coprocessor with 12 pipeline stages and 8-way decode capability. A time counter and register scoreboard deterministically schedule instructions by tracking operand readiness and hazard information, it reduces wasted issue slots. The system predicts latency windows for memory operations, scheduling independent instructions for high utilization and it simplifies hardware, reduces power consumption and avoids pipeline flushes.