Ensuring my Design Verification is ISO26262 Compliant

DVClub India Meeting – May 2024

Event at a Glance

Tuesday 28 th May , 2024

12:00 – 16:00 (IST)

Venue – Cadence Design System, Bengaluru & Online

FREE to attend Online and In -person

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Ensuring my Design Verification is ISO26262 Compliant

With the widespread of the modern automobiles, run and regulated by automotive ECUs, the need for advanced safety features has also become inevitable. And this is why today modern vehicles are required to adhere to the safety standards listed within the Automotive Safety Integrity Level (ASIL).
In this DVClub meeting our speakers will share their experiences on how to ensure safety of automotive with ISO 26262 standard.

Agenda (IST):

Time	Session Description	Slides	Videos
12:00 IST	Welcome and Introduction Tessolve
12:00 IST	Speaker 1 – Tessolve Semiconductor
12:30 IST	Speaker 2 – External Speaker
13:00 IST	Lunch & Networking
14:00 IST	Ensuring my Design Verification is ISO26262 Compliant Sundararajan Ananthakrishnan, Cadence Design Systems
15:00 IST	Speaker 4 – External Speaker
16:00 IST	Refreshments & Networking

About DVClub

The principal goal of each DVCLUB meeting is to have fun while helping build the European verification community through regular educational and networking events. Attendance at DVClub Europe meetings is free and is open to all non-service provider semiconductor professionals. Each meeting addresses a specific issue faced by the design and verification community and whatever your speciality provides an excellent opportunity for updating knowledge as well as share experiences, insights and issues with other members of the verification community.

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.

Ensuring my Design Verification is ISO26262 Compliant

DVClub India Meeting – May 2024

Event at a Glance

Tuesday 28 th May , 2024

12:00 – 16:00 (IST)

Venue – Cadence Design System, Bengaluru & Online

FREE to attend Online and In -person

Join DVClub India

Ensuring my Design Verification is ISO26262 Compliant

Agenda (IST):

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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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