ECE PhD Dissertation Defense: Jinghan Zhang

PhD Dissertation Defense: Domain Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications

Jinghan Zhang

Location: ISEC 362 or 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 to achieve the needed efficiency and/or performance.
Platforms integrating many accelerators (ACCs) is a primary approach for efficient, high-performance stream computing.
However, designing one platform for each application is not economical due to the high costs of nonrecurring engineering (NRE) and time-to-market (TTM).
To this end, the concept of domain platforms is proposed, which takes advantage of similarities across applications and designs one unified platform to accelerate a domain of applications instead of focusing on a single reference application.
This dissertation approaches designing domain platforms from a function-level (kernel-level) acceleration through a heterogeneous ACC-rich platform, where each ACC is specialized to accelerate a particular function.
There is a great challenge to select ACCs allocated in the domain platform, considering the large design space and performance balance across many applications.
However, current Design Space Exploration (DSE) tools only focus on an individual application in isolation (e.g., one particular vision flow) for allocating a platform, but not a set of similar applications.
This dissertation introduces Greedy Guided Mutation (GGM) to speed up the mutation in the GIDE algorithm, which calculates an ACC score according to current allocation to guide mutation.
Alternatively, Rapid Domain Platform Performance Prediction (RDP^3) methods are introduced to replace a large number of the slow platform assessment in domain DSE, which avoids the complex application binding exploration.
In the experiments, GGM reduces 84.8% of exploration time with a 0.23% loss of the final OpenVX domain platform’s performance.
RDP^3 using a machine learning method yields an even more significant speedup, saving 90.8% of exploration time with only 0.0003% performance loss.
DmDSE is a milestone to broaden DSE scope from individual applications to the domain level. It tremendously pushes the domain platform design from manually and engineering experience guided into a general automatic process.