SimPyler: A Compiler-Based Simulation Framework for Machine Learning Accelerators

Co-optimization of hardware and software in modern deep neural network (DNN) systems can be performed using design space exploration (DSE) tools. Leveraging estimation models to predict design decisions' impact on the system's performance allows a fast evaluation of up to billions of architectural choices. In this work, we propose SimPyler, an end-to-end framework for latency estimations of DNN workloads on machine learning (ML) accelerators. SimPyler represents DNN kernels as graphs executed on abstract accelerator models to simulate the system's latency. By generating the entire simulation infrastructure automated from the DNN operator description, the framework can flexibly adjust to changes at the hardware or algorithmic level, enabling the usage in DSE applications. A key enabler in this automation process is a machine-learning compiler. The framework is implemented in Python, using only open-source software. We demonstrate and validate the proposed methodology by modeling different single-core and multi-core hardware architectures and DNNs, comparable to state-of-the-art. Our experiments show we can estimate the end-to-end latency with an average error of 4.12%