Pragmatic Formal Verification Methodology for Clock Domain Crossing (CDC)

• URL: http://arxiv.org/abs/2406.06533v1
• Date: Sat, 20 Apr 2024 13:17:25 GMT
• Title: Pragmatic Formal Verification Methodology for Clock Domain Crossing (CDC)
• Authors: Aman Kumar, Muhammad Ul Haque Khan, Bijitendra Mittra,
• Abstract summary: This paper focuses on the development of a pragmatic formal verification methodology to minimize the Clock Domain Crossings (CDC) issues. CDCs are prone to metastability effects and functional verification of such CDC is very important to ensure that no bug escapes.
• Score: 2.3624953088402734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern System-on-Chip (SoC) designs are becoming more and more complex due to the technology upscaling. SoC designs often operate on multiple asynchronous clock domains, further adding to the complexity of the overall design. To make the devices power efficient, designers take a Globally-Asynchronous Locally-Synchronous (GALS) approach that creates multiple asynchronous domains. These Clock Domain Crossings (CDC) are prone to metastability effects, and functional verification of such CDC is very important to ensure that no bug escapes. Conventional verification methods, such as register transfer level (RTL) simulations and static timing analysis, are not enough to address these CDC issues, which may lead to verification gaps. Additionally, identifying these CDC-related bugs is very time-consuming and is one of the most common reasons for costly silicon re-spins. This paper is focused on the development of a pragmatic formal verification methodology to minimize the CDC issues by exercising Metastability Injection (MSI) in different CDC paths.

Related papers

• Analogous Alignments: Digital "Formally" meets Analog [0.0]
  This paper focuses on the pragmatic formal verification of a mixed signal Intellectual Property (IP) that has a combination of digital and analog blocks. Digital and Analog Mixed-Signal (AMS) designs, which are fundamentally different in nature, are integrated seamlessly in a formal verification setup.
  arXiv  Detail & Related papers  (2024-09-23T13:38:31Z)
• Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing [4.420321822469078]
  This article introduces a causality-driven sequence segmentation model. It segments the sequence into different phases based on the sudden shifts in causal mechanisms that occur during phase transitions. A soft sensing model called temporal-causal graph convolutional network (TC-GCN) is trained for each phase.
  arXiv  Detail & Related papers  (2024-06-30T10:40:54Z)
• Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter [57.64003871384959]
  This work presents a new approach to fast context-biasing with CTC-based Word Spotter. The proposed method matches CTC log-probabilities against a compact context graph to detect potential context-biasing candidates. The results demonstrate a significant acceleration of the context-biasing recognition with a simultaneous improvement in F-score and WER.
  arXiv  Detail & Related papers  (2024-06-11T09:37:52Z)
• CCDM: Continuous Conditional Diffusion Models for Image Generation [22.70942688582302]
  Continuous Conditional Generative Modeling (CCGM) aims to estimate the distribution of high-dimensional data, typically images, conditioned on scalar continuous variables. Existing Conditional Adversarial Networks (CcGANs) were initially designed for this task, their adversarial training mechanism remains vulnerable to extremely sparse or imbalanced data. To enhance the quality of generated images, a promising alternative is to replace CcGANs with Conditional Diffusion Models (CDMs)
  arXiv  Detail & Related papers  (2024-05-06T15:10:19Z)
• Pragmatic Formal Verification of Sequential Error Detection and Correction Codes (ECCs) used in Safety-Critical Design [3.1367764768902613]
  ECCs are often used in digital designs to protect data integrity. Exhaustive verification of ECC using formal methods has been a challenge. We present a pragmatic formal verification approach of complex ECC cores.
  arXiv  Detail & Related papers  (2024-04-28T18:31:09Z)
• NAC-TCN: Temporal Convolutional Networks with Causal Dilated Neighborhood Attention for Emotion Understanding [60.74434735079253]
  We propose a method known as Neighborhood Attention with Convolutions TCN (NAC-TCN) We accomplish this by introducing a causal version of Dilated Neighborhood Attention while incorporating it with convolutions. Our model achieves comparable, better, or state-of-the-art performance over TCNs, TCAN, LSTMs, and GRUs while requiring fewer parameters on standard emotion recognition datasets.
  arXiv  Detail & Related papers  (2023-12-12T18:41:30Z)
• Adaptive Planning Search Algorithm for Analog Circuit Verification [53.97809573610992]
  We propose a machine learning (ML) approach, which uses less simulations. We show that the proposed approach is able to provide OCCs closer to the specifications for all circuits.
  arXiv  Detail & Related papers  (2023-06-23T12:57:46Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• Dual-Cross Central Difference Network for Face Anti-Spoofing [54.81222020394219]
  Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Central difference convolution (CDC) has shown its excellent representation capacity for the FAS task. We propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features.
  arXiv  Detail & Related papers  (2021-05-04T05:11:47Z)
• Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition [46.69852287267763]
  This article describes an efficient training method for online streaming attention-based encoder-decoder (AED) automatic speech recognition (ASR) systems. The proposed method significantly reduces recognition errors and emission latency simultaneously. The best MoChA system shows performance comparable to that of RNN-transducer (RNN-T)
  arXiv  Detail & Related papers  (2021-02-28T08:17:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.