Using Neural Networks to Select Test Cases for Coverage Closure

Conference:	Verification Futures 2022 (click here to see full programme)
Speaker:	Tim Blackmore
Presentation Title:	Using Neural Networks to Select Test Cases for Coverage Closure
Abstract:	We compare two recently published methods for using neural networks to select test cases in order to close coverage more quickly. Both methods rely on the identification of tests that contrast with, or are novel with respect to, the tests that have so far been simulated. Results of the use of the two methods on the same industrial-strength data set are reported and show that both approaches can lead to considerable savings in time to close coverage. This indicates that the novelty of a test can be used as a proxy for the likelihood that a test hits new coverage. Since determining novelty using machine learning techniques uses only open source licenses and is typically much quicker than simulation this has potential to reduce cost and schedules for projects for which coverage closure is time-consuming.
Speaker Bio:	Tim is a Senior Principal Verification Engineer at Infineon Technologies, based in Bristol. His research focus is improving the efficiency of the verification of complex IPs, subsystems and SoCs including by the use of machine learning. He is also the Global Verification Manager for Infineon’s current family of Aurix3G microcontrollers and is a Visiting Industrial fellow at the University of Bristol.
Key Points:	Comparison of two machine learning techniques for novel test selection. Results of using the two methods to speed coverage closure for a significant data set indicate novelty is a reasonable proxy for hitting new coverage. Potential for real-life savings in cost and schedule for projects for which coverage closure is time-consuming.

ML Verification Turns Convention on its Head

The verification of processor architectures designed for Machine Learning (ML) applications represent a departure from conventional techniques. Conventional constrained random testbenches, which focus on stimulus driving coverage, cannot scale for many ML algorithm realizations. ML architectures involve neural networks of processors that “learn” by manipulating coefficients across the network to match ideal outputs to a large quantity of input data. Furthermore, smart compiler technology is employed to leverage the many paths available in the network. An effective verification strategy can leverage planning algorithms that start with the desired output and optimize input values to achieve that output. Ensuring the paths that the compiler might trigger have all been tested, and that the test content can scale from individual processors to the entire network are critical challenges. Breker will share various approaches to this problem, developed through cooperation with three noted AI processor providers.

3 Key Points:

Current verification methodologies cannot scale to meet ML processor challenges

ML verification approach: consider desired outputs, optimize inputs to match

Test Suite Synthesis enable planning algorithm approach to target ML requirements

An Emulation Strategy for Artificial Intelligence Designs

The emergence of Artificial Intelligence is the “next big thing” and presents a unique opportunity for disruptive semiconductor development. End applications could range from ADAS, to 3D facial recognition, to voice and image processing, or to intelligent search. The SoCs for AI applications whether targeted for training or inference will have their own unique characteristics, but present quite common verification challenges that we will present in this session.

Supporting designs as big as 15 billion gates, Mentor’s Veloce Strato has unique virtualization capabilities that enable highly accurate pre-silicon execution of AI benchmarking applications like MLPerf. The Veloce Power App enables analysis of peak and average. We will cover how Veloce Strato and its supporting solutions are the best tool to help address the verification challenges of SoCs targeted for AI applications.

3 Key Points:

Deterministic solution for AI chips verification

Full virtual solution for HW/SW verification

TERAOPS/Watt assessement prior silicon availability

AI chips must get the math right

Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks are often based on operations that use multiplication and addition of floating-point values. FPUs are difficult to implement. The IEEE 754 standard defines many corner-case scenarios and non-ordinary values. Even a minor rounding mistake could accumulate over many iterations and produce a large error. An FPU formal verification app compliant with IEEE-754 provides an efficient and rigorous solutions to FPU functional verification

3 Key Points:

Floating-point unit (FPU) for AI chips

FPU Formal Verification App

Compliance with IEEE-754

Name: Mike Bartley

Designation: Senior Vice President – VLSI Design

Title: Introduction

Biography:

Mike Bartley has a PhD in Mathematics from Bristol University, an MSc in Software Engineering, an MBA from the Open University and over 25 years of experience in software testing and hardware verification. He has built and managed state-of-the-art test and verification teams in a number of companies who still use the methodologies he established. Since founding TVS in 2008 he has grown the company to over 100 employees worldwide. Dr Bartley is Chair of both the Bristol branch of the British Computer Society and the West of England Bristol Local Enterprise Partnership (LEP). He has had over 50 articles and presentations published on the subjects of hardware verification, software testing and outsourcing.

Using Neural Networks to Select Test Cases for Coverage Closure

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ML Verification Turns Convention on its Head

An Emulation Strategy for Artificial Intelligence Designs

AI chips must get the math right

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