Friday, November 3, 2023 | 11:00 AM
Uncas A. Whitaker Hall, 218
6760 Forest Park Pkwy, St. Louis, MO 63105, USA
Department of Computer Science
We entrust our lives to computing systems. Whenever we hop on a flight, suddenly brake in a car to avoid a collision, or fight a disease in an intensive care unit, our lives depend on a computing system doing the right thing (logical correctness) at the right time (timeliness). Timeliness is certainly key for safety-critical real-time systems to meaningfully interact with the physical world. But as modern lifestyles become symbiotic with computer systems, we also expect timeliness from the graphical interfaces in our mobile devices, data streaming services, and edge cloud systems. Unfortunately, timeliness is a property that cannot be explicitly programmed, as it emerges from the interplay of software and hardware components. Without explicit control over one such interplay, timeliness remains an elusive dimension.
This begs the question: can we move away from application-agnostic systems? Can we, instead, leverage fine-grained knowledge of said interplay to regain control over our applications’ timeliness?
In this talk, I will outline the vision for "software-shaped platforms," or SOSH platforms for short. At the core of the SOSH paradigm is the idea of exposing direct control over the flow of data exchanged between hardware components in embedded System-on-Chips (SoC). Data flow manipulation primitives are constructed in reprogrammable hardware and interposed between central processors, memory modules, and I/O devices. A new layer of system software is then introduced to leverage such primitives and to achieve fine-grained control and introspection over the interaction of SoC resources. Turning memory and I/O data flows into manageable entities unlocks a new degree of internal awareness in complex systems. We first review our recent works that explore foundational mechanisms to implement data flow manipulation primitives to be employed in SOSH platforms. Next, we outline future research avenues revolving around the SOSH paradigm for workload profiling and prediction and to implement advanced memory semantics.