RISCV CPU Verification – Opportunities and Challenges

Conference:	Verification Futures 2023 (click here to see full programme)
Speaker:	Divyang Agrawal
Presentation Title:	RISCV CPU Verification - Opportunities and Challenges
Abstract:	The highly configurable nature of RISCV ISA makes it uniquely suited for a hierarchical verification methodology covering both architectural and microarchitectural complexity. This technical talk will focus on how Tenstorrent leveraged on the lessons from x86 and ARM to build a modular and scalable CPU verification framework. It will also preview how design complexity has to be tackled looking at silicon as a starting point. And ultimately why robust open source RISCV verification collateral is essential for broader adoption of the ISA from microcontrollers to high performance datacenter class products
Speaker Bio:	Divyang Agrawal is a Senior Director at Tenstorrent where he works on RISCV Cores focusing on design verification, emulation, architectural tools and methodologies. He has previously worked on x86 and ARM architectures. Prior to Tenstorrent, Divyang worked at AMD where he held leadership roles within AMD's CPU Cores team working on several generations of high-performance cores. He also led the CPU Power Management IPs and Silicon Validation for all AMD cores. Divyang has a BTech in EE from Nagpur, India and an MBA from University of California at Berkeley
Key Points:	What can RISCV verification learn from decades of effort on x86 and ARM verification The open nature of RISCV ISA offers tremendous opportunities and interesting challenges. Can we meet some of these by collaboration across the open source community DV complexity can leverage on ISA flexibility

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.

RISCV CPU Verification – Opportunities and Challenges

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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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