Surinder Sood

Surinder Sood

Sale, England, United Kingdom
1K followers 500+ connections

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Experience

Education

  • The University of Auckland Graphic

    The University of Auckland

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    I focussed on model based design, verification and validation of hybrid systems. I developed co-simulation and formal techniques that could co-simulate and validate hardware and software hybrid system models. The most recent technique, which uses formal verification, is used not only to verify cyber-physical system models but digital systems as well.

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Volunteer Experience

  • Rotary International Graphic

    Chartered member

    Rotary International

    - Present 2 years 9 months

    Education

  • IEEE Graphic

    Senior Member

    IEEE

    - Present 2 years 6 months

    Science and Technology

Publications

  • Robust hardware-software Co-simulation framework for design and validation of Hybrid Systems

    MEMOCODE'22

    Model based design of embedded controllers is prevalent across different industries. The final step in model based design is synthesis of hardware (or software) controller and then testing the synthesized controller in closed-loop with the plant model - this is termed as co-simulation. Standard cosimulation approaches use asynchronous communication fabric. However, they are known to suffer from race conditions, jitter, etc, making real-time property validation difficult. Current approaches to…

    Model based design of embedded controllers is prevalent across different industries. The final step in model based design is synthesis of hardware (or software) controller and then testing the synthesized controller in closed-loop with the plant model - this is termed as co-simulation. Standard cosimulation approaches use asynchronous communication fabric. However, they are known to suffer from race conditions, jitter, etc, making real-time property validation difficult. Current approaches to co-simulation problems either require complex middle-ware or require synthesis of the controller and plant for synchronous execution. However, these approaches are unsuited for hybrid system control design and validation, as they require the plant model to execute at an arbitrarily small simulation step, while the synthesized controller executes at its own rate if any. The small simulation step slows down the simulation and such a setup does not guarantee level crossing detection. In this paper, we propose a novel Metric Interval Temporal Logic (MITL) based validation and Hardware in Loop (HIL) co-simulation framework, which synchronizes and integrates the controller synthesized in hardware and the plant executing in software. A discrete controller handles a level crossing generated by the plant, which evolves on variable step size. The traces generated from the closed-loop operation of the overall system are used to validate MITL properties. Finally, the controller hardware and the plant model are adjoined via a communication architecture, whose sample time is dependent upon the robustness estimates of the MITL properties, which is necessary to guarantee validation correctness.

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  • Formal verification of Deep Neural Networks in Hardware

    IEEE WintechCon'22

    Deep neural network (DNN) verification is an emerging field. The problem is more pronounced when they are modelled as hardware logic. Existing formal verification solutions for such hardware designs are fraught with issues like scalability and compute intensive resources. We solve this problem by creating a lightweight verification infrastructure and verifying the DNN logic by way of equivalence check. The light weight formal verification model is created by applying neural network…

    Deep neural network (DNN) verification is an emerging field. The problem is more pronounced when they are modelled as hardware logic. Existing formal verification solutions for such hardware designs are fraught with issues like scalability and compute intensive resources. We solve this problem by creating a lightweight verification infrastructure and verifying the DNN logic by way of equivalence check. The light weight formal verification model is created by applying neural network simplification approaches discussed in the paper. The DNN formal model as a consequence is made simpler by removing the unwanted nodes, as a result it is capable of verifying intricate deep neural network designs effectively as compared with simulation based conventional strategies. The approach is applied on a design which is a car collision avoidance system (CARCAS) which is a logic deployed in autonomous car driving paradigm.

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  • Novel AI based pre-silicon Performance estimation and validation of complex System-on Chip

    IEEE CSDE 2021,AsiaPacific conference,Brisbane,Australia

  • A Formal Approach to SoC Integration Validation

    Design Automation Conference-2021

  • Novel end to end Non-coherent access mechanism on X86 SOC

    Design Automation Conference- Sanfracisco

  • CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

    Cornell University Library,USA, 2013

  • Formal verification contract based micro architectural analysis of Server SoC's

    DAC 2022,San Francisco, USA

  • Let's be Formal while talking about verification quality: A novel approach to qualify assertion based VIPs

    DVCON-2018

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  • Robust model based design and validation of Cyber-physical Systems

    In Proceedings of TECS-ACM’19 . ACM, New York, NY, USA

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