Time Dilation and Contraction for Programmable Analog Devices with Jaunt

Abstract

Programmable analog devices are a powerful new computing substrate that are especially appropriate for performing computationally intensive simulations of neuromorphic and cytomorphic models. Current state of the art techniques for configuring analog devices to simulate dynamical systems do not consider the current and voltage operating ranges of analog device components or the sampling limitations of the digital interface of the device.We present Jaunt, a new solver that scales the values that configure the analog device to ensure the resulting analog computation executes within the operating constraints of the device, preserves the recoverable dynamics of the original simulation, and executes slowly enough to observe these dynamics at the sampled digital outputs. Our results show that, on a set of benchmark biological simulations, 1) unscaled configurations produce incorrect simulations because they violate the operating ranges of the device and 2) Jaunt delivers scaled configurations that respect the operating ranges to produce correct simulations with observable dynamics.

Noise-Aware Dynamical System Compilation for Analog Devices with Legno

“…We evaluate Legno on twelve benchmarks, six of which were previously hand-implemented by our collaborators and six of which are novel or from prior work [1,2]. Unless otherwise stated, each benchmark executes for 20 simulation units.…”

“…Arco [2] and Jaunt [1] both target simulated programmable analog devices for dynamical systems. Legno, in contrast, targets a physical analog device and must effectively deal with all of the physical phenomena (noise, frequency and operating range constraints, quantization errors, manufacturing variations) present in physical analog devices.…”

“…The rewrite rules perform the following operations. The rules presented in rows 1-4 and 6 were adapted from prior work [1]:…”

GPkit: A Human-Centered Approach to Convex Optimization in Engineering Design

“…It did so by embracing a participatory human-centered framework focused on validating workers' knowledge [26] and using connections between that knowledge and the mathematics of optimization to develop GPkit, a toolkit for convex Geometric Programs. GPkit was used by many engineers and researchers during its development [1,2,11,14,16,17,27,28,32,33,36,46,48,49,53,60,64,68], which resulted in features that are:…”