Silicon Technology

Leading The Engineering Innovation

Silicon Engineering

Extended arm of semiconductor companies

Tessolve is a leading end-to-end solution provider, from chip design, test and PCB engineering through embedded systems development. We enable customers with a faster time-to-market through deep domain expertise in Analog, Digital, Mixed Signal, and RF, broad ATE platform experience and built-in infrastructure including a test floor, characterization, reliability lab and PCB FAB. Tessolve allows customers to leverage high end engineering in a cost effective model by being able to scale teams to meet exact customer needs.

Tessolve’s Engineering Solutions

Chip design to production – all under one roof

Chip Solutions

Hardware Solutions

Chip Solutions

Analog and Digital expertise at advanced process nodes

Our VLSI design team provide customers solutions from RTL to GDS II. With our digital and analog expertise combined, we are uniquely positioned to address a variety of chips at advanced process nodes. We know how to take your chip to market by providing strategy along with implementation. We can integrate into your existing team or become an extension of your process. As a value-add our deep test expertise allows us to plan a DFT strategy that ensures faster silicon bring up.

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Hardware Solutions

Optimized hardware to meet development needs

Hardware is a critical piece of Chip and Systems development. We have over 200 PCB designers who can design and develop ATE boards, System boards, Reliability and Evaluation boards. Our domain expertise includes RF, Mixed Signal, Digital and Analog boards. We have built the necessary infrastructure required to optimize our HW designs including library development, BOM management, and simulations.

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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

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

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

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.

Silicon Technology

Leading The Engineering Innovation

Silicon Engineering

Extended arm of semiconductor companies

Tessolve’s Engineering Solutions

Chip design to production – all under one roof

Chip Solutions

Hardware Solutions

Chip Solutions

Analog and Digital expertise at advanced process nodes

Hardware Solutions

Optimized hardware to meet development needs

Solutions

Sector & Support

Insights

ML Verification Turns Convention on its Head

AI chips must get the math right

An Emulation Strategy for Artificial Intelligence Designs

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