ECE MS Thesis Defense: Jinghan Zhang

MS Thesis Defense: Allocating One Common Accelerator-Rich Platform for Many Streaming Applications

Jinghan Zhang

Location: Zoom Link

Abstract: Many demanding streaming applications share functional and structural similarities with other applications in their respective domain, e.g. video analytics, software-defined radio, and radar. This opens the opportunity for specialization (e.g. heterogeneous computing) to achieve the needed efficiency and/or performance. However, current Design Space Exploration (DSE) focuses on an individual application in isolation (e.g. one particular vision flow), but not a set of similar applications. Hence, optimizations that occur due to considering multiple applications simultaneously are missed. New DSE methodologies and tools are needed with a broader scope of application sets instead of individual applications.
This thesis introduces a novel Domain DSE approach focusing on streaming applications. Key contributions are: (1) a formalized method to extract the functional and structural similarities of domain applications, (2) domain application generation to provide enough synthetic domains as study cases, (3) a rapid platform performance estimation and comparison at two abstraction levels: Domain Score (DS) and Analytic Performance Estimation (APE) model, (4) a methodology to evaluate a platform’s benefit for a set of applications, and (5) two novel algorithms, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE), for hardware/software partitioning of a domain-specific platform to maximize the throughput across domain applications (under certain constraints).
This thesis demonstrates DSS’s and GIDE’s benefits using OpenVX applications and synthetic domains. The DSS and GIDE generated domain-specific platforms improve performance over application-specific platforms by 58%, and 75% for OpenVX, as well as by 23% and 48% for synthetic applications. GIDE’s platforms reach 99.8% (OpenVX) and 97.6% (synthetic) throughput of the domain optimal platform obtained through exhaustive search.