Efficient Verification of a RADAR SoC Using Formal and Simulation-Based Methods
- URL: http://arxiv.org/abs/2404.15371v1
- Date: Sat, 20 Apr 2024 13:16:55 GMT
- Title: Efficient Verification of a RADAR SoC Using Formal and Simulation-Based Methods
- Authors: Aman Kumar, Mark Litterick, Samuele Candido,
- Abstract summary: This paper presents a case study based on our work to verify a complex Radio Detection And Ranging (RADAR) based SOC.
We leverage both formal and simulation-based methods to complement each other and achieve verification sign-off with high confidence.
- Score: 2.1626093085892144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As the demand for Internet of Things (IoT) and Human-to-Machine Interaction (HMI) increases, modern System-on-Chips (SoCs) offering such solutions are becoming increasingly complex. This intricate design poses significant challenges for verification, particularly when time-to-market is a crucial factor for consumer electronics products. This paper presents a case study based on our work to verify a complex Radio Detection And Ranging (RADAR) based SoC that performs on-chip sensing of human motion with millimetre accuracy. We leverage both formal and simulation-based methods to complement each other and achieve verification sign-off with high confidence. While employing a requirements-driven flow approach, we demonstrate the use of different verification methods to cater to multiple requirements and highlight our know-how from the project. Additionally, we used Machine Learning (ML) based methods, specifically the Xcelium ML tool from Cadence, to improve verification throughput.
Related papers
- Optimizing Coverage-Driven Verification Using Machine Learning and PyUVM: A Novel Approach [2.3624953088402734]
The complexity of System-on-Chip (SoC) designs has created a bottleneck in verification.
Existing verification techniques rely on time-consuming and redundant simulation regression.
We propose a novel methodology that leverages supervised Machine Learning (ML) to optimize simulation regressions.
arXiv Detail & Related papers (2025-02-23T17:54:23Z) - What Really Matters for Learning-based LiDAR-Camera Calibration [50.2608502974106]
This paper revisits the development of learning-based LiDAR-Camera calibration.
We identify the critical limitations of regression-based methods with the widely used data generation pipeline.
We also investigate how the input data format and preprocessing operations impact network performance.
arXiv Detail & Related papers (2025-01-28T14:12:32Z) - Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO [0.8520624117635328]
This paper explores the application of supervised machine learning models to tackle activity detection issues.
We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks.
The results are compelling: the algorithm achieves an exceptional 99% accuracy rate, confirming its efficacy in real-world applications.
arXiv Detail & Related papers (2024-06-11T11:08:33Z) - A Novel Generative AI-Based Framework for Anomaly Detection in Multicast Messages in Smart Grid Communications [0.0]
Cybersecurity breaches in digital substations pose significant challenges to the stability and reliability of power system operations.
This paper proposes a task-oriented dialogue system for anomaly detection (AD) in datasets of multicast messages.
It has a lower potential error and better scalability and adaptability than a process that considers the cybersecurity guidelines recommended by humans.
arXiv Detail & Related papers (2024-06-08T13:28:50Z) - Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers [2.258538713779673]
This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing.
Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals.
Our model achieves an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML. 2018+ dataset.
arXiv Detail & Related papers (2024-03-08T21:33:03Z) - Effective Communication with Dynamic Feature Compression [25.150266946722]
We study a prototypal system in which an observer must communicate its sensory data to a robot controlling a task.
We consider an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level.
We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase.
arXiv Detail & Related papers (2024-01-29T15:35:05Z) - Task-Oriented Sensing, Computation, and Communication Integration for
Multi-Device Edge AI [108.08079323459822]
This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC)
We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
arXiv Detail & Related papers (2022-07-03T06:57:07Z) - Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems.
Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity.
We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
arXiv Detail & Related papers (2022-05-16T12:14:42Z) - Model-based Deep Learning Receiver Design for Rate-Splitting Multiple
Access [65.21117658030235]
This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods.
The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead.
Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
arXiv Detail & Related papers (2022-05-02T12:23:55Z) - Learning-Based UE Classification in Millimeter-Wave Cellular Systems
With Mobility [67.81523988596841]
Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves.
For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns.
Research to date has demonstrated efficient ways of machine learning based UE classification.
arXiv Detail & Related papers (2021-09-13T12:00:45Z) - Improved Speech Emotion Recognition using Transfer Learning and
Spectrogram Augmentation [56.264157127549446]
Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction.
One of the main challenges in SER is data scarcity.
We propose a transfer learning strategy combined with spectrogram augmentation.
arXiv Detail & Related papers (2021-08-05T10:39:39Z) - Machine Learning based Indicators to Enhance Process Monitoring by
Pattern Recognition [0.4893345190925177]
We propose a novel framework for machine learning based indicators combining pattern type and intensity.
In a case-study from semiconductor industry, our framework goes beyond conventional process control and achieves high quality experimental results.
arXiv Detail & Related papers (2021-03-24T10:13:20Z) - Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with
Heterogeneous Learning Tasks [53.1636151439562]
Mobile edge computing (MEC) provides a natural platform for AI applications.
We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS)
Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
arXiv Detail & Related papers (2020-12-25T07:08:50Z)
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.