MAGMA: Mixture-Model Adaptive Gaussian Model Acceleration

Abstract

Conventional FPGA-based Gaussian Mixture Model (GMM) accelerators use offline-trained, fixed parameters, limiting their ability to adapt to evolving scene statistics in long-lived edge systems. We present MAGMA, a fully synthesizable fixed-point FPGA architecture that performs concurrent GMM inference and online Expectation-Maximization (EM) parameter adaptation from a streaming RGB pixel input. MAGMA combines a pipelined inference datapath with a background update engine using hardware-friendly transcendental approximations---a range-reduced Chebyshev exponential, a CLZ-based logarithm, and a shift-and-subtract divider---alongside guards against variance collapse and cluster death that stabilize online fixed-point EM. Implemented on an AMD Spartan-7 XC7S50 with $K=4$ clusters, MAGMA runs at 74.49~MHz using 7,779 LUTs, 91 DSPs, and no block RAM, consuming 274~mW. It achieves an $11.8\times$ inference speedup and an $81\times$ M-step speedup over software, while spatial subsampling reduces per-update pixel volume by $40\times$ with minimal impact on EM convergence. Under a synthetic non-stationary scene, MAGMA's online adaptation improves mean pixel accuracy over a static baseline (81.5\% vs.\ 79.7\%), demonstrating that full online GMM learning is achievable on a commodity edge FPGA.