A CIM-based Gaussian Random Number Generator for Edge Devices as Security Primitive

Efficient generation of high-quality Gaussian random numbers remains a key challenge for enabling differential privacy (DP) and security primitives on resource-constrained edge devices. Existing hardware-based solutions rely on lookup tables or complex transformations, making them ill-suited for tightly integrated computation-in-memory (CIM) architectures. This work proposes a Gaussian random number generator (GRNG) based on a digital 8T SRAM-CIM array that leverages intrinsic memory periphery to generate and transform entropy. The design exploits voltage underscaling and stochastic readpath failures to produce in-situ uniform randomness, which is then aggregated through a lightweight central limit theorem (CLT)-based datapath with digital recentering to synthesize N0, σ2) samples. By reusing existing periphery, the proposed approach eliminates the need for dedicated random number generation hardware. Experimental evaluation demonstrates that the generated distribution closely follows an ideal normal curve, achieving low tail error and strong goodness-of-fit across standard statistical tests. The design sustains ≈ 2 Gsamples s throughput at an energy of ≈ 11.2 fJ/ sample, with no additional area overhead beyond standard CIM periphery.