Design Space Exploration

Design problems are ubiquitous in scientific and industrial achievements. Scientists design experiments to gain insights into natural phenomena, and engineers design machines to execute tasks more efficiently. These choices are often complex, high-dimensional, and interdependent, which makes it hard for developers to reason about. For instance, optimizing code generation for hardware accelerators requires several design choices that trade off performance, energy consumption, and resource usage.

We introduce a new methodology and corresponding software framework, HyperMapper, which handles multi-objective optimization, unknown feasibility constraints, discrete variables, and injection of user prior knowledge. These features are common requirements in the optimization of design choices for code generation and HyperMapper is the only framework that simultaneously supports them all. In this tutorial we will introduce HyperMapper and give comprehensive hands-on examples of these features.

We will present multiple use-cases of HyperMapper highlighting the importance of design-space exploration. First, we will present a use-case of HyperMapper on the automatic tuning of hardware accelerators within the recently introduced Spatial programming language and compiler, minimizing design run-time under the constraint of the design fitting in a target FPGA. We will also present a use-case on Homunculus, a high-level compiler for data-plane ML, on the automatic generation of compliant, high-performance ML models for datacenter networks mapped to a reconfigurable fabric in data plane devices.