Fast Performance Estimation and Design Space Exploration of Manycore-based Neural Processors

In the design of a neural processor, a cycle-accurate simulator is usually built to estimate the performance before hardware implementation. Since using the simulator to perform design space exploration (DSE) of hardware architecture is quite time consuming, we propose a novel method to use a high-level analytical model for fast DSE. In the model, non-deterministic execution delay is modeled with some parameters whose contribution to the performance is estimated statically by simulation. The viability of the proposed methodology is confirmed with two neural processors with different manycore architectures, achieving 2000 times speed-up within 3% accuracy error, compared with simulator-based DSE.