skip to main content

Title: An Efficient Meta-Reinforcement Learning Approach for Circuit Linearity Calibration via Style Injection
Circuit linearity calibration can represent a set of high-dimensional search problems if the observability is limited. For example, linearity calibration of digital-to-time converters (DTC), an essential building block of modern digital phaselocked loops (DPLLs), is an example of a high-dimensional search problem as difficulty of measuring ps delays hinders prior methods that calibrate stage by stage. And, a calibrated DTC can become nonlinear again due to changes in temperature (T) and power supply voltage (V). Prior work reports a deep reinforcement learning framework that is capable of performing DTC linearity calibration with nonlinear calibration banks; however, this prior work does not address maintaining calibration in the face of temperature and supply voltage variations. In this paper, we present a meta-reinforcement learning (RL) method that can enable the RL agent to quickly adapt to a new environment when the temperature and/or voltage change. Inspired by the Style Generative Adversarial Networks (StyleGANs), we propose to treat temperature and voltage changes as the styles of the circuits. In contrast to traditional methods employing circuit sensors to detect changes in T and V, we utilize a machine learning (ML) sensor, to implicitly infer a wide range of environmental changes. The style information from the ML sensor is subsequently injected into a small portion of the policy network, modulating its weights. As a proof of concept, we first designed a 5-bit DTC at the normal voltage (1V) and normal temperature (27℃) corner (NVNT) as the environment. The RL agent begins its training in the NVNT environment. Following this initial phase, the agent is then tasked with adapting to environments with different temperature and supply voltages. Our results show that the proposed technique can reduce the Integral Non-Linearity (INL) to less than 0.5 LSB within 10, 000 search steps in a changed environment. Compared to starting learning from a random initialized policy and a trained policy, the proposed meta-RL approach takes 63% and 47% fewer steps to complete the linearity calibration, respectively. Our method is also applicable to the calibration of many other kinds of analog and RF circuits.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ;
Publisher / Repository:
Date Published:
Journal Name:
MidWest Symposium on Circuits and Systems
Page Range / eLocation ID:
10 to 14
Subject(s) / Keyword(s):
["Meta-Reinforcement Learning, Circuit Linearity Calibration, Style Generative Adversarial Networks, Fast Adaptation."]
Medium: X
Tempe, AZ, USA
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance. However, prior work that seeks to find the optimal shared control ratio needs an accurate human model, which is usually challenging to obtain. In this study, the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient (DDPG) (TD3X)‐based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally. The robot's states, shared control ratio in the previous time step, and human's control input is used as inputs to the reinforcement learning (RL) agent, which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model. Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio. Furthermore, to accelerate the training process and protect the robot, a navigation demonstration policy and a safety guard are developed. A neural network (NN) structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed. In addition, an extended DAGGER (DAGGERX) human agent is developed for training the RL agent to reduce human workload. Robot simulations and experiments with humans in the loop are conducted. The results show that the DAGGERX human agent can simulate real human inputs in the worst‐case scenarios with a mean square error of 0.0039. Compared to the original TD3 agent, the TD3X‐based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment. The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment, while the performance is equally good in the complex environment because of the use of an advanced human agent. In the human subject tests, participants' average perceived workload was significantly lower in shared control than that in exclusively manual control (26.90 vs. 40.07,p = 0.013).

    more » « less
  2. Carbon nanotube (CNT) field-effect transistors (CNFETs) promise significant energy efficiency benefits versus today's silicon-based FETs. Yet despite this promise, complementary (CMOS) CNFET analog circuitry has never been experimentally demonstrated. Here we show the first reported demonstration of full CNFET CMOS analog circuits. For characterization, we fabricate analog building block circuits: multiple instances of two-stage op-amps. These CNFET CMOS op-amps achieve gain >700 (maximum derivative of output voltage with respect to differential input voltage), operate at a scaled sub- 500 mV supply voltage, achieve high linearity (even when operating at these scaled voltages), and are robust over time (minimal drift over >10,000 cycled measurements over 12 hours). Additionally, we demonstrate a front-end analog sub-system that integrates a CNFET-based breath sensor with an analog sensor interface circuit (transimpedance amplifier followed by a voltage follower to convert resistance change of the chemoresistive CNFET sensor into a buffered output voltage). These experimental demonstrations are the first reports of CNFET CMOS analog functionality that is essential for a future CNT CMOS technology. 
    more » « less
  3. Decision-making under uncertainty (DMU) is present in many important problems. An open challenge is DMU in non-stationary environments, where the dynamics of the environment can change over time. Reinforcement Learning (RL), a popular approach for DMU problems, learns a policy by interacting with a model of the environment offline. Unfortunately, if the environment changes the policy can become stale and take sub-optimal actions, and relearning the policy for the updated environment takes time and computational effort. An alternative is online planning approaches such as Monte Carlo Tree Search (MCTS), which perform their computation at decision time. Given the current environment, MCTS plans using high-fidelity models to determine promising action trajectories. These models can be updated as soon as environmental changes are detected to immediately incorporate them into decision making. However, MCTS’s convergence can be slow for domains with large state-action spaces. In this paper, we present a novel hybrid decision-making approach that combines the strengths of RL and planning while mitigating their weaknesses. Our approach, called Policy Augmented MCTS (PA-MCTS), integrates a policy’s actin-value estimates into MCTS, using the estimates to seed the action trajectories favored by the search. We hypothesize that PA-MCTS will converge more quickly than standard MCTS while making better decisions than the policy can make on its own when faced with nonstationary environments. We test our hypothesis by comparing PA-MCTS with pure MCTS and an RL agent applied to the classical CartPole environment. We find that PC-MCTS can achieve higher cumulative rewards than the policy in isolation under several environmental shifts while converging in significantly fewer iterations than pure MCTS. 
    more » « less
  4. Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy. The metapolicy, when adapted over only a small (or just a single) number of steps, is able to perform near-optimally on a new, related task. However, a major challenge to adopting this approach to solve real-world problems is that they are often associated with sparse reward functions that only indicate whether a task is completed partially or fully. We consider the situation where some data, possibly generated by a suboptimal agent, is available for each task. We then develop a class of algorithms entitled Enhanced Meta-RL using Demonstrations (EMRLD) that exploit this information—even if sub-optimal—to obtain guidance during training. We show how EMRLD jointly utilizes RL and supervised learning over the offline data to generate a meta-policy that demonstrates monotone performance improvements. We also develop a warm started variant called EMRLD-WS that is particularly efficient for sub-optimal demonstration data. Finally, we show that our EMRLD algorithms significantly outperform existing approaches in a variety of sparse reward environments, including that of a mobile robot. 
    more » « less
  5. Abstract

    Supervised machine learning via artificial neural network (ANN) has gained significant popularity for many geomechanics applications that involves multi‐phase flow and poromechanics. For unsaturated poromechanics problems, the multi‐physics nature and the complexity of the hydraulic laws make it difficult to design the optimal setup, architecture, and hyper‐parameters of the deep neural networks. This paper presents a meta‐modeling approach that utilizes deep reinforcement learning (DRL) to automatically discover optimal neural network settings that maximize a pre‐defined performance metric for the machine learning constitutive laws. This meta‐modeling framework is cast as a Markov Decision Process (MDP) with well‐defined states (subsets of states representing the proposed neural network (NN) settings), actions, and rewards. Following the selection rules, the artificial intelligence (AI) agent, represented in DRL via NN, self‐learns from taking a sequence of actions and receiving feedback signals (rewards) within the selection environment. By utilizing the Monte Carlo Tree Search (MCTS) to update the policy/value networks, the AI agent replaces the human modeler to handle the otherwise time‐consuming trial‐and‐error process that leads to the optimized choices of setup from a high‐dimensional parametric space. This approach is applied to generate two key constitutive laws for the unsaturated poromechanics problems: (1) the path‐dependent retention curve with distinctive wetting and drying paths. (2) The flow in the micropores, governed by an anisotropic permeability tensor. Numerical experiments have shown that the resultant ML‐generated material models can be integrated into a finite element (FE) solver to solve initial‐boundary‐value problems as replacements of the hand‐craft constitutive laws.

    more » « less