Presentation
Probabilistic Memory Design for Efficient Trustworthy Edge Intelligence
DescriptionProbabilistic computation plays an important role in trustworthy edge intelligence to quantify uncertainty, enhance robustness, reconstruct data and protect privacy, but its adoption is limited by orders of magnitude data throughput gap between Gaussian random number generation(GRNG) and computation, as well as instruction overhead. This paper introduces \emph{probabilistic memory} (p-MEM), a unified memory primitive that stores distribution parameters and samples directly at native memory bandwidth where deterministic data becomes the zero-variance special case. Using a layout-validated p-MEM simulator, we comprehensively explore device choices, memory specifications, and technology nodes, showing that p-MEM can achieve $>1000$\,GSa/s/mm$^2$ GRNG throughput (including memory arrays). Integrated into CPU / GPU systems, p-MEM reduces instruction count by up to $2.19\times$/$4.37\times$, sampling latency by $562\times$/$3.45\times$, and energy by $295.5\times$/$3.53\times$ for BNN workloads, providing a scalable hardware substrate for trustworthy probabilistic AI.
Event Type
Research Manuscript
TimeMonday, July 2711:50am - 12:03pm PDT
LocationMtg Room 203AB