{"ID":5676767,"CreatedAt":"2026-07-03T03:29:23.032456456Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02465","arxiv_id":"2607.02465","title":"Probabilistic Memory for Trustworthy Edge Intelligence","abstract":"Probabilistic 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 the orders-of-magnitude data throughput gap between Gaussian random number generation (GRNG) and computation, as well as instruction overhead. This paper introduces probabilistic memory (p-MEM), a unified memory primitive that stores distribution parameters, such as mean and standard deviation, and samples directly at the 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 more than 1000 GSa/s/mm^2 GRNG throughput, including memory-array access. Integrated into CPU/GPU systems, p-MEM reduces instruction count by up to 2.19x/4.37x, sampling latency by 562x/3.45x, and energy by 295.5x/3.53x for Bayesian neural network workloads, providing a scalable hardware substrate for trustworthy probabilistic AI.","short_abstract":"Probabilistic 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 the orders-of-magnitude data throughput gap between Gaussian random number generation (GRNG) and computation, as well as i...","url_abs":"https://arxiv.org/abs/2607.02465","url_pdf":"https://arxiv.org/pdf/2607.02465v1","authors":"[\"Likai Pei\",\"Jiahao Zheng\",\"Xueji Zhao\",\"Emilie Ye\",\"Jianbo Liu\",\"Hanqing Tao\",\"Ming-Yen Lee\",\"Ruiyang Qin\",\"Yiyu Shi\",\"Shimeng Yu\",\"X. Sharon Hu\",\"Ningyuan Cao\"]","published":"2026-07-02T17:31:17Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}