Python Automation

Tessolve are experts in using the Python to help automate software testing and have extensive hands on experience across a variety of projects and implementations and always focus on the benefits and ROI.

What is Python?

Python is a high-level back-end programming language that is commonly used in software testing for automation. Python is: powerful; fast; integrates well; is easy to learn and is Open source.

A Brief History of Python

Python 1.0 was released in 1994 when its creator Guido van Rossum was confronted by the shortcomings of ABC language.
In 2000, Python’s core development team released Python 2.0 with many improvisations including a garbage collector and support for Unicode.
Python 3.0 was released in 2008 a multi-paradigm language offering developers the options of object-orientation, structured and functional programming.
Python has multiple implementations including: Jython, scripted in Java language for Java Virtual Machine; Cpython, which is written in C and is the most popular implementation of Python.
Today, Python is still very popular for companies that require: rapid prototyping and development of applications; integration of data analysis and statistical techniques into workflows; complex machine learning, statistical analysis, or data manipulation and mining.

Python Testing with Tessolve

The Python testing experts
Vast practical hands on experience across variety of projects and implementations.
Automation Centre of excellence in UK and India
All automation consultants cross trained in Python and other common languages
Automation delivery is focused on business benefits and ROI

Case Study

A Bespoke Automation Framework

Find our how the development of a bespoke automation framework for an innovative VR company helped them meet their goals.

Reduced overhead of managing testing by 60%+
Enabled transformation to continuous integration process
Demonstrable full testing by linking features to tests
Measurably improved quality of software by adding independence in the testing process
Decreased software maintenance costs through automated re-test

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Contact one of our consultants today to discuss your requirements.
No hard sales, just pertinent questions to understand your needs and to discuss how we may be able to help.

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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.

Python Automation

What is Python?

A Brief History of Python

Python Testing with Tessolve

Case Study

A Bespoke Automation Framework

Find Out More

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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