HyST-DNA: Hybrid SRAM-TCAM DNN accelerator with genetic optimization on FPGA

Abstract

Edge intelligence applications demand high-throughput and energy-efficient hardware accelerators under strict power, latency, and resource constraints in real-time environments. Conventional compute-centric architectures rely heavily on sequential multiply-accumulate (MAC) operations and frequent off-chip memory access, resulting in high energy consumption, increased latency, and bandwidth bottlenecks. These limitations become more pronounced as neural network models scale in size and complexity. While Ternary Content-Addressable Memory (TCAM) offers an alternative through parallel associative search and constant-time pattern matching, it suffers from excessive power consumption and limited adaptability due to continuous match-line activation. This work proposes a hybrid SRAM-TCAM memory-centric deep neural network (DNN) architecture that integrates three tightly coupled subsystems. First, a two-layer autoencoder with ReLU activations compresses 128-bit input feature vectors to 32-bit latent codes, achieving a compression ratio η=4×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta = 4\times $$\end{document} and a 75%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$75\%$$\end{document} BRAM saving. Second an on-chip dual-port BRAM data-locality buffer exploits the compressed representation to eliminate inter-layer off-chip DRAM accesses in the critical inference path. Third, a 256×32\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$256 \times 32$$\end{document}-bit Ternary Content-Addressable Memory array provides O(1) parallel associative inference. A software-only Genetic Algorithm (GA) with Roulette Wheel Selection (RWS) optimizes TCAM configuration offline prior to FPGA deployment; the converged 8,480-bit chromosome is loaded once at startup and remain static during inference, adding zero dynamic power overhead. The proposed architecture, implemented on a Xilinx Artix-7 FPGA, achieves a 42.3%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$42.3\%$$\end{document} reduction in power compared to a conventional MAC-based baseline (120.5 mW vs. 208.7 mW), an end-to-end throughput of 108.47 Mops reflecting steady-state pipelined operation at 108.47 MHz (2.42×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.42\times $$\end{document} speedup), and an inference accuracy of 95.3%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95.3\%$$\end{document} – a 1.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.5\%$$\end{document} degradation attributable to quantization, evaluated on the benchmark dataset. Power figures are obtained from the Vivado Power Estimator tool (post-implementation) at nominal supply voltage of 1 V. These results demonstrate an efficient and scalable solution for next-generation edge AI inference systems.