K-PACT: Kernel Planning for Adaptive Context Switching -- A Framework for Clustering, Placement, and Prefetching in Spectrum Sensing
H. Umut Suluhan, Jiahao Lin, Serhan Gener, Chaitali Chakrabarti, Umit Ogras, Ali Akoglu

TL;DR
This paper introduces K-PACT, a kernel planning framework that enables rapid context switching in spectrum sensing hardware by clustering and preloading kernels, significantly reducing latency and data movement overhead.
Contribution
It presents a novel multi-objective optimization-based planner for mapping spectrum sensing workflows onto reconfigurable hardware, enabling fast runtime adaptation with minimal overhead.
Findings
Reduces off-chip binary fetches by 207.81x
Lowers average switching time by 98.24x
Improves per-subband execution time by 132.92x
Abstract
Efficient wideband spectrum sensing requires rapid evaluation and re-evaluation of signal presence and type across multiple subchannels. These tasks involve multiple hypothesis testing, where each hypothesis is implemented as a decision tree workflow containing compute-intensive kernels, including FFT, matrix operations, and signal-specific analyses. Given dynamic nature of the spectrum environment, ability to quickly switch between hypotheses is essential for maintaining low-latency, high-throughput operation. This work assumes a coarse-grained reconfigurable architecture consisting of an array of processing elements (PEs), each equipped with a local instruction memory (IMEM) capable of storing and executing kernels used in spectrum sensing applications. We propose a planner tool that efficiently maps hypothesis workflows onto this architecture to enable fast runtime context switching…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
