AI 中文总结
本文提出一种存算一体启发的概率硬件ProbSplat，可独立控制高斯混合分量的均值与方差，在三维场景重建中实现低复杂度、低功耗的高斯溅射计算，获21.99dB PSNR的重建保真度，适用于边缘端AR/VR与机器人场景。
AI 中文摘要
本文提出了ProbSplat，一种基于可编程且能效高的浮栅反相器列的存算一体（Compute-in-Memory, CIM）启发式架构，用于概率计算。相较于作者此前的工作，ProbSplat可对高斯混合分量的均值和方差进行编程与存储，并在场景重建过程中计算高斯溅射的对数似然，能效极高，适用于边缘端的机器人技术及增强/虚拟现实（AR/VR）场景。所提方案通过确定性调整浮栅MOSFET的阈值电压，实现了对均值和方差的独立控制，提升了硬件对概率分布的编程保真度。该设计采用180nm CMOS工艺，在1.8V电压、50MHz频率下进行仿真，在三维高斯混合建模过程中，均值-方差独立性的偏差小于2.4%。与传统数字实现相比，ProbSplat大幅降低了计算复杂度、内存占用和功耗；该可扩展框架在三维GMM中处理500个混合函数时，采用4位精度的对数似然推理能耗仅为18pJ，采用8位精度时，基于ProbSplat特性的场景重建达到了21.99dB的峰值信噪比（PSNR），具备令人满意的保真度。
英文摘要
This paper presents ProbSplat, a Compute-in-Memory (CIM)-inspired architecture based on programmable and energy efficient floating-gate inverter columns for probabilistic computing. Improving upon our prior work, ProbSplat programs and stores both means and variances of Gaussian mixture components, and evaluates log-likelihood for gaussian splatting during scene reconstruction with high energy efficiency, suitable for robotics and augmented/virtual reality (AR/VR) at the edge. Our proposed scheme enables independent control of both mean and variance via deterministic adjustment of floating-gate MOSFET threshold voltages, increasing the fidelity of hardware to program probability distributions. The design is simulated in 180nm CMOS on 1.8 V at 50 MHz and achieves mean-variance independence with <2.4% deviation during 3-D Gaussian mixture modeling. Compared to conventional digital implementations, ProbSplat significantly reduces compute complexity, memory footprint, and power consumption. The scalable framework consumes 18pJ energy per log-likelihood inference with 4-bit precision while operating for 500 mixture functions in a 3-D GMM. Scene reconstruction with ProbSplat's characteristics gave satisfactory fidelity of 21.99 PSNR (dB) at 8-bit precision.
CommentsAccepted for publication in the 2026 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
