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Systematic and Structural Methods for Post-Silicon Validation

Systematic and Structural Methods for Post-Silicon Validation
用于硅后验证的系统性和结构性方法
批准号:
RGPIN-2015-05312
负责人:
Nicolici, Nicola
金额:
$2.7万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

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中文摘要
翻译
在过去十年中,保证电子电路和系统达到既定或隐含目标的难度不断增加。这主要是由于状态空间对不断增长的状态元素数量的指数依赖,以及在功率和能量限制下促进性能改进所需的过多的时钟和功率域。预硅验证通常用于确保设计与其规格之间的一致性。在抽条之前,可以测量的东西受到模拟时间和精度的限制,当置信度被认为足够时,设计就被发布用于制造。制造测试的重点是筛选每个制造器件的物理缺陷;考虑到它的参考是设计实现,制造测试并不关心发现和识别已经逃到硅原型中的细微设计错误(或bug)。因此,在pre-silicon阶段中使用的验证任务继续在这些早期的硅原型上进行,这一术语通常被称为post-silicon验证。由于模拟比实际的硅原型慢6到9个数量级,因此实际上不可能计算先验的黄金响应。再加上缺乏内部节点访问,这使得后硅验证成为一个棘手的问题。此外,最先进的电路和系统可能在一次实验中失败,而在随后的实验中正常运行。这是因为逃到硅原型上的细微设计错误通常是由不易重复的事件引起的。例如,它们可以由异步接口和/或电路工作模式的动态变化引起的罕见交互触发,例如,为了响应不同的工作负载或环境条件而权衡功率与性能。考虑到上述情况,研究新的系统方法,可以帮助后硅验证任务,可以为更广泛的电子工业带来显著的好处,就生产力的提高和更重要的是,最终产品的质量而言。***作为本研究计划的一部分,我们的目标是研究通用后硅验证系统的所有关键构建模块的自动化方法:跟踪收集和分析,事件检测,后硅刺激生成和应用,以及覆盖测量。所有这些都依赖于设计结构而不是功能。我们的立场是,结构化方法将提供急需的理论基础,这将促进整个半导体行业自动化方法的无缝可移植性,以及下一代集成电路的可扩展性,这些集成电路有望在运行时积极地权衡功耗和性能。**
英文摘要
The difficulty of guaranteeing that electronic circuits and systems meet the stated or implied goals has been continuously increasing over the past decade. This is mainly due to the exponential dependence of the state space on the growing number of state elements, as well as the excessively large number of clock and power domains needed to facilitate performance improvements under power and energy constraints. Pre-silicon verification is commonly employed to ensure the consistency between the design and its specification. Before tapeout what can be measured is limited by the simulation time and accuracy, and designs are released for manufacturing when the confidence level is deemed sufficient. Manufacturing test is focused on screening for physical defects in each fabricated device; considering that its reference is the design implementation, manufacturing test is not concerned with finding and identifying subtle design errors (or bugs) that have escaped to silicon prototypes. Thus the verification tasks employed during the pre-silicon phase continue on these early silicon prototypes, a term commonly referred to as post-silicon validation.***Because simulation is 6 to 9 orders of magnitude slower than the actual silicon prototype, it is not practically possible to compute golden responses a-priori. Together with the lack of internal node access, this makes post-silicon validation an intractable problem. Furthermore, state-of-the-art circuits and systems might fail in one experiment and operate correctly in the subsequent ones. This is because subtle design errors that escape to the silicon prototypes are often excited by not-easily-repeatable events. For example, they can be triggered by rare interactions caused by asynchronous interfaces and/or dynamic changes in the circuit's operating mode, e.g., for trading-off power vs performance in response to varying workload or environmental conditions. Considering the above, investigating new systematic approaches that can assist the post-silicon validation tasks can bring significant benefits to the broader electronics industry, in terms of both productivity gains and, more importantly, the quality of the final product.***As part of this research program, we aim to investigate automated methods for all the key building blocks of a general-purpose post-silicon validation system: trace collection and analysis, event detection, post-silicon stimuli generation and application, and coverage measurement. All of the above will rely on the design structure rather than its functionality. It is our position that a structural approach will provide the much-needed theoretical foundations, which will facilitate seamless portability of automated methods across the semiconductor industry, as well as scalability to the next-generation of integrated circuits which are expected to aggressively trade off power vs performance at run-time. **
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Fault-Tolerant Computing for Machine Learning Applications
  • 批准号:
    RGPIN-2020-06884
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.4万
  • 财政年份:
    2022
  • 负责人:
    Nicolici, Nicola
  • 依托单位:
Fault-Tolerant Computing for Machine Learning Applications
  • 批准号:
    RGPIN-2020-06884
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.4万
  • 财政年份:
    2021
  • 负责人:
    Nicolici, Nicola
  • 依托单位:
Fault-Tolerant Computing for Machine Learning Applications
  • 批准号:
    RGPIN-2020-06884
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.4万
  • 财政年份:
    2020
  • 负责人:
    Nicolici, Nicola
  • 依托单位:
Systematic and Structural Methods for Post-Silicon Validation
  • 批准号:
    RGPIN-2015-05312
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.7万
  • 财政年份:
    2019
  • 负责人:
    Nicolici, Nicola
  • 依托单位:
国内基金
海外基金
Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位: