Quantum leap for Automotive R&D

Quantum Computing Inc. (QCi) is making waves in the automotive industry. The company has sold its EmuCore reservoir computing device to a major global automotive manufacturer for research and growth purposes. EmuCore, leveraging FPGA technology, excels in:

• Time series prediction
• Image recognition
• Other serial data applications

Its key advantages include high speed, low power consumption, and cost efficiency.

This deployment is a precursor to QCi’s upcoming PCIe-based photonic reservoir computing units, designed for AI-intensive and sensor-rich edge environments. The sale underscores the growing interest in QCi’s machine learning solutions, particularly in edge computing applications where energy efficiency and rapid processing are paramount.

According to QCi’s CEO, Dr. William McGann, the deal validates QCi’s technology and signals rising demand across diverse industries.

Nextchip integrates Ceva’s AI for Smarter ADAS

Nextchip is set to enhance its next-generation advanced driver assistance systems (ADAS) by licensing Ceva’s NeuPro-M Edge AI Neural Processing Unit (NPU) IP. Nextchip, a developer of automotive image and ADAS solutions, aims to boost AI performance, accuracy, and efficiency in vehicle safety technologies through this integration.

The NeuPro-M offers robust support for Vision Transformers (ViTs), enabling precise object recognition and scene understanding, even in complex environments. Its scalable architecture, ranging from 4 to 400 TOPs per core, allows parallel processing of multiple video streams and AI models, making it ideal for high-performance automotive applications.

The global demand for ADAS is projected to reach $122.8 billion by 2030. This collaboration positions Nextchip to deliver safer, more reliable automotive systems. Ceva’s NeuPro-M NPU also features robust DSP capabilities and an AI SDK for streamlined model optimization and deployment across edge devices.

cadence and TSMC: Powering Next-Gen Silicon

Cadence Design Systems and TSMC have expanded their partnership to advance next-generation silicon development across cutting-edge process technologies, including 3D-IC integration and advanced-node design. This collaboration builds on Cadence’s AI-driven design portfolio, featuring certified design flows and silicon-proven IP for TSMC’s N2P, N3, and N5 nodes, along with the latest A16 and N3C technologies. The joint effort aims to accelerate time to silicon for high-performance computing,automotive,and infrastructure AI applications.

Key highlights include:

• Expanded support for TSMC’s 3DFabric® ecosystem, with complete chiplet-to-system solutions.
• Enhanced IP for AI training markets (e.g., HBM3E and UCIe).
• Improved quality of results through Cadence’s Integrity™ 3D-IC platform.

Cadence is also strengthening its automotive design capabilities with IP certified for TSMC’s N5A and N3A, optimized for autonomous and software-defined vehicles.

Additionally, Cadence is advancing “More-than-Moore” innovations in RF, photonics, and analog design, while leveraging cloud-based, GPU-accelerated computing for next-generation efficiency. Together, Cadence and TSMC are enabling customers to overcome the increasing complexity of semiconductor design with faster development cycles, optimized performance, and power efficiency.

Cadence has strengthened its automotive technology portfolio through expanded collaboration with TSMC, introducing certified IP for advanced process nodes N5A and N3A tailored to next-generation automotive applications. These solutions support critical systems such as ADAS, autonomous driving, and software-defined vehicles. The portfolio includes high-performance automotive-grade IP such as LPDDR5X-9600, PCIe 5.0, CXL 2.0, 25G-KR, and 10G multi-protocol SerDes, all optimized for the stringent reliability and performance demands of the automotive industry.

AECC Explores Digital Twins for Enhanced Vehicle Services

The Automotive Edge Computing Consortium (AECC) has released a new white paper, Digital Twin Use Cases for Automobiles, spotlighting how digital twins and edge computing can transform connected vehicle services. These virtual replicas of physical systems enable real-time simulation, enhancing road safety, driving experiences, and environmental efficiency.

The paper outlines three main applications:

• Road traffic optimization
• Personalized cruise assist
• Vehicle resource sharing

It also details the edge computing technologies needed to support these systems,including data offloading and opportunistic transfers. AECC invites organizations to join its Proof of concept program and membership network to advance connected mobility innovation.

FAQ: Connected Car Technology

What is edge computing?

Edge computing brings computation and data storage closer to the location where it is needed,improving response times and saving bandwidth.

What are digital twins?

Digital twins are virtual replicas of physical systems that enable real-time simulation and analysis.

What is ADAS?

ADAS stands for Advanced Driver Assistance systems, which are electronic technologies that assist drivers in driving and parking functions.