Automated Generation of Variability-Aware Approximate Circuits to Eliminate Timing Guardbands

Core Concepts

By employing approximate computing principles, our automated framework generates variability-aware approximate circuits that eliminate the need for timing guardbands, while introducing negligible functional error.

Abstract

The content presents an automated framework for generating variability-aware approximate circuits that eliminate the need for timing guardbands. The key highlights are:

The framework operates on the gate-level netlist of any combinational circuit and leverages approximate computing techniques to reduce the critical path delay (CPD) and its variability under process variations.

Variability-aware standard cell libraries are created to accurately capture the impact of process variations on circuit behavior. These libraries are fully compatible with commercial EDA tools.

A high-level delay estimator is proposed that stochastically traverses the netlist's directed acyclic graph (DAG) to model the delay distribution across multiple candidate critical paths under process variations.

A multi-objective genetic algorithm is employed to efficiently explore the design space of possible approximate circuits and derive a Pareto-optimal set of solutions with reduced delay variability and minimal functional error.

Experimental results show that the generated variability-aware approximate circuits can be reliably operated under process variations without timing guardbands, while introducing a negligible functional error of merely 5.3 × 10^-3.

Stats

The worst-case CPD obtained from a 1000-point Monte-Carlo analysis for the baseline circuits ranges from 0.07ns to 0.46ns.
The worst-case NMED of the baseline circuits ranges from 1e-2 to 1e-3.

Quotes

"By introducing negligible functional error of merely 5.3 × 10^-3, our variability-aware approximate circuits can be reliably operated under process variations without sacrificing the application performance."

Key Insights Distilled From

Variability-Aware Approximate Circuit Synthesis via Genetic Optimization

by Kons... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04258.pdf

Deeper Inquiries

How can the proposed framework be extended to handle sequential circuits and other types of reliability degradations beyond process variations?

The proposed framework can be extended to handle sequential circuits by incorporating state elements such as flip-flops and latches into the netlist representation. This would involve modeling the timing behavior of sequential elements and their interactions with the combinational logic in the circuit. Additionally, the framework can be enhanced to address other types of reliability degradations beyond process variations, such as aging effects and temperature variations. This would require incorporating models for these degradation mechanisms into the delay estimation and approximation algorithms, allowing for a more comprehensive analysis of circuit reliability.

What are the potential limitations of the stochastic DAG traversal approach in accurately modeling the critical path delay distribution under process variations?

One potential limitation of the stochastic DAG traversal approach is the assumption of independence between the random variables representing gate delays in the DAG. In reality, the delays of interconnected gates may exhibit correlations that are not captured by this approach, leading to inaccuracies in the estimation of the critical path delay distribution. Additionally, the complexity of the DAG traversal algorithm may increase significantly with the size and complexity of the circuit, potentially impacting the scalability and efficiency of the delay estimation process. Furthermore, the accuracy of the delay estimation heavily relies on the quality of the gate delay models and the statistical information used to annotate the DAG, which may introduce uncertainties and errors in the estimation process.

Can the framework be further optimized to reduce the computational complexity and enable real-time deployment of the generated approximate circuits?

To reduce computational complexity and enable real-time deployment of the generated approximate circuits, several optimizations can be considered. One approach is to refine the approximation candidate selection process by incorporating more sophisticated heuristics or machine learning techniques to efficiently prune the design space and focus on high-impact approximations. Additionally, optimizing the genetic algorithm parameters, such as population size and mutation rates, can improve convergence speed and solution quality. Implementing parallel processing techniques to leverage multi-core architectures can also enhance the scalability and speed of the optimization process. Furthermore, exploring hardware acceleration or dedicated hardware implementations for critical components of the framework can further reduce computation time and enable real-time deployment of the approximate circuits.