期刊
出版社
IEEE
DOI: 10.1109/VLSID49098.2020.00021
关键词
Kernel density estimation; inverse transform sampling; cumulative density function; operational transconductance amplifier; Sigmoid kernel
资金
- Semiconductor Research Corporation (SRC) [2712.022]
In this work, we present a CMOS-based sampling circuit to produce random numbers of non-parametric densities for Monte Carlo-based methods. Scaling of purely algorithmic Monte Carlo sampling techniques with problem size is challenging and this severely compromises performance of the application. To overcome this challenge, we present a novel CMOS-based Monte Carlo sampler which can be considered as an add-on to the existing random number generator (RNG) circuits to produce non-parametric Monte Carlo samples. Our design employs two statistical techniques, namely: (i) kernel Density estimation (KDE) and (ii) Inverse Sampling. We present a novel CMOS operational transconductance amplifier (OTA) based Sigmoid kernel implementation for non-parametric cumulative density function (CDF) estimation using KDE. Subsequently, we show that inverse sampling can be realized using successive approximation based mixed-signal implementation. OTAs are designed to operate in sub-threshold regime while consuming 300nW. Discussed architecture allows programmability of number of random variable (RV) samples (N-samp) and Sigmoid kernel standard deviation (sigma(kernal)) to ensure reliable CDF estimation. Overall architecture on an average consumes similar to 750/mu W while using 50 random samples for CDF estimation at 200 MHz clock frequency.
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