AI for Coding

Conference:	Verification Futures 2018 (click here to see full programme)
Speaker:	Darren Royle, Development Team Lead, Diffblue
Presentation Title:	AI for Coding
Abstract:	More and more of the world’s business and public services are running on software – from obvious fields like communication and banking, to newer areas of transportation and health care, making us reliant on software for critical parts of our lives. As a result, bad software can cause damage to all of us, whether it be financially or even physically, and our need for robust and reliable testing is as crucial as it has ever been. I’d like to highlight some of these areas where we at Diffblue believe an AI agent can be of massive benefit, and how we are integrating into a range of different software development methodologies and approaches. Automating part of the test writing process can facilitate more robust and secure software whilst increasing the pace of development. I would like to show how this translates to everyday development practices. Three key topics covered: Artificial Intelligence in software testing Self writing code Unit testing
Speaker Bio:	Darren is a classical ‘full-stack’ web developer, having implemented and managed huge SaaS applications for almost 15 years within the educational field. Today he leads the integration team within Diffblue, bringing ‘AI for code’ to the web and providing seamless integration into developers workflows and development lifecycles.

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.

AI for Coding

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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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