RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task
RL
- URL: http://arxiv.org/abs/2207.06412v1
- Date: Wed, 13 Jul 2022 04:06:32 GMT
- Title: RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task
RL
- Authors: Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Pan, Song Han, Nan
Sun
- Abstract summary: We present Robust Analog, a robust circuit design framework that involves the variation information in the optimization process.
We show that Robust Analog can significantly reduce required optimization time by 14-30 times.
- Score: 19.71047877921737
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Analog/mixed-signal circuit design is one of the most complex and
time-consuming stages in the whole chip design process. Due to various process,
voltage, and temperature (PVT) variations from chip manufacturing, analog
circuits inevitably suffer from performance degradation. Although there has
been plenty of work on automating analog circuit design under the typical
condition, limited research has been done on exploring robust designs under
real and unpredictable silicon variations. Automatic analog design against
variations requires prohibitive computation and time costs. To address the
challenge, we present RobustAnalog, a robust circuit design framework that
involves the variation information in the optimization process. Specifically,
circuit optimizations under different variations are considered as a set of
tasks. Similarities among tasks are leveraged and competitions are alleviated
to realize a sample-efficient multi-task training. Moreover, RobustAnalog
prunes the task space according to the current performance in each iteration,
leading to a further simulation cost reduction. In this way, RobustAnalog can
rapidly produce a set of circuit parameters that satisfies diverse constraints
(e.g. gain, bandwidth, noise...) across variations. We compare RobustAnalog
with Bayesian optimization, Evolutionary algorithm, and Deep Deterministic
Policy Gradient (DDPG) and demonstrate that RobustAnalog can significantly
reduce required optimization time by 14-30 times. Therefore, our study provides
a feasible method to handle various real silicon conditions.
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