Join us to explore such concepts and more, where we use Python to leverage its library-rich environment feasible for verification using Vyoma’s UpTickPro platform, in this edition of Capture the Bug hackathon, organized by NIELIT, Calicut, mentored by IIT Madras, in association with VLSI System Design and Vyoma Systems.
Hackathon details – https://nielithackathon.in/
For the upcoming Capture The Bug Hackathon, participant is free to choose any Verilog Open Source design from any website/reference similar to that of OpenCores with appropriate licensing which is compatible with Icarus Verilog and also the Verilog code needs to be synthesizable.
Happy Verification!!!
In a nutshell, the project really is to build a Verilator Verification environment i.e. a structure in which we can set up testbenches that are executed with Verilator. The thing which is interesting in this project is we are going to tie that Verilator piece with a golden model arithmetic library and that is going to be something that you can publish as nobody else in the world has that
It’s a Verilator Testbench environment that uses an online arithmetic library to generate the right bit pattern. We are not using randoms, but we are using a Golden model. If you progress from ALU to a vector accelerator, you will have a vector lane, vector register file, vector load/store unit, vector instructions.
Design Verification is critical to proving functional correct- ness and establishing confidence in a design. Several stud- ies from industry and academia, particularly over the course of the last two decades, have explored various verifica- tion methodologies that fall somewhere between dynamic or purely static formal approaches.
System-on-Chips (SoCs) today have become extremely complex structures housing heavily optimized cores, count- less peripherals, and large interconnect fabrics. Even re- stricting ourselves to just verifying the microprocessor, the state space to be verified is enormous and cannot be exhaus- tively explored in any finite amount of time. Manually writ- ten tests, while effective at capturing some complexities of design intent, suffer from the fact that they are expensive in cost and time required to develop them. Random stimulus methods perform better because they eventually cover many cases. Most new ideas in dynamic verification over the last two decades have largely been towards semi formal verifi- cation methodologies such as coverage driven verification and constrained test generation. In this paper, we explore an approach to dynamic functional verification that we use at the RISE lab, IIT Madras for the verification of the RISC-V based Shakti cores.
Design Verification is critical to proving functional correctness and establishing confidence in a design. Several studies from industry and academia, particularly over the course of the last two decades, have explored various verification methodologies that fall somewhere between dynamic or purely static formal approaches.
Random stimulus methods perform better because they eventually cover many cases. Most new ideas in dynamic verification over the last two decades have largely been towards semi formal verification methodologies such as coverage driven verification and constrained test generation. In this paper, we explore an approach to dynamic functional verification that we use at the RISE lab, IIT Madras for the verification of the RISC-V based Shakti cores.
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