Design Space Exploration in High-Level Synthesis

Decanato - Facoltà di scienze informatiche

Data: 30 Ottobre 2020 / 16:30 - 18:00

You are cordially invited to attend the PhD Dissertation Defense of Lorenzo Ferretti on Friday October 30th, 2020 at 16:30
Please note that given the updated Covid-19 restrictions, the Dissertation Defense will be held online.
You can join here

High Level Synthesis (HLS) is a process which, starting from a high-level description of an application (C/C++), generates the corresponding RTL code describing the hardware implementation of the desired functionality. The HLS process is usually controlled by user-given directives (e.g., directives to set whether or not to unroll a loop) which influence the resulting implementation area and latency. By using HLS, designers are able to rapidly generate different hardware implementations of the same application, without the burden of directly specifying the low level implementation in detail. Nonetheless, the correlation among directives and resulting performance is often difficult to foresee and to quantify, and the high number of available directives leads to an exponential explosion in the number of possible configurations. In addition, sampling the design space involves a time-consuming hardware synthesis, making a brute-force exploration infeasible beyond very simple cases. However, for a given application, only few directive settings result in Pareto-optimal solutions (with respect to metrics such as area, run-time and power), while most are dominated. The design space exploration problem aims at identifying close to Pareto-optimal implementations while synthesising only a small portion of the possible configurations from the design space. In this dissertation I present an overview of the HLS design flow, followed by a discussion about existing strategies in literature. Moreover, I present new exploration methodologies able to automatically generate optimised implementations of hardware accelerators. The proposed approaches are able to retrieve a close approximation of the real Pareto solutions while synthesising only a small fraction of the possible design, either by smartly navigating their design space or by leveraging prior knowledge. Herein, I also present a database of design space explorations whose goal is to push the research boundaries by offering to researchers a tool for the standardisation of exploration evaluation, and a reliable source of knowledge for machine learning based approaches. Lastly, the stepping-stones of a new approach relying on deep learning strategies with graph neural networks is presented, and final remarks about future research directions are discussed.

Dissertation Committee:
- Prof. Laura Pozzi, Università della Svizzera italiana, Switzerland (Research Advisor)
- Prof. Cesare Alippi, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Antonio Carzaniga, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Nikil Dutt, UC Irvine, United States (External Member)
- Prof. Paolo Ienne, EPFL, Switzerland (External Member)