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EE Times Current

EE Times on the electronics industry, from semiconductors and AI hardware to design tools, for design engineers and other technology professionals.

278 episodes · 157 hours · since 2018

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EE Times Current is filed under electronics, engineering and hardware. Its most recent episode published on , so it is still actively publishing. Listeners have left 28 ratings on Apple Podcasts, averaging 4.8 out of 5.

Latest episodes

The 10 newest episodes of EE Times Current, each with a summary and chapters.

  • · 24m

    Beyond PPA: How Foundation IP Enables Predictable Execution and Lower TCO for AI-Era SoCs

    Ravi Todi, Executive Director of Product Management at Synopsys, explains that competitive power, performance, and area (PPA) are no longer sufficient for AI-era SoCs, as design complexity and execution risk at advanced nodes require a focus on predictable execution and total cost of ownership (TCO). He details how Synopsys' Foundation IP platform—integrating embedded memory, logic libraries, and I/O—reduces integration risks and avoids expensive re-spins, which can cost $20–$40 million in mask sets and delay tape-outs by 6–9 months. Todi highlights that this holistic approach enables first-pass silicon success, cross-foundry portability, and optimized PPA for both high-performance compute (HPC) and edge AI (HAI) designs, shifting the buying decision from lowest IP license price to lowest total cost of silicon on time.

    Chips, FPGAs & semiconductorsPricing, margins & unit economics
    7 chapters

  • · 57m

    Why Computing With Time Gives Neuromorphic AI an Edge

    Dr. Ryad Benosman, co-founder of Prophesee and researcher at Johns Hopkins, explains why neuromorphic engineering and event-based vision offer a superior path for AI compared to traditional frame-based computer vision. He details his journey from pure mathematics to building event sensors, commercializing them through Prophesee, and developing retinal prosthetics using optogenetics and silicon implants. Benosman argues that computing with time allows for efficient, low-power processing that mimics biological systems, whereas current AI hits a wall due to power and data inefficiency.

    AI & machine learningSensors & measurement in products
    8 chapters

  • · 23m

    Where Software Meets Silicon: A Digital Twin Playbook

    VJ Chobisa, Senior Director at Siemens EDA, explains how digital twins and virtual platforms enable early software-hardware co-development to address system-level verification challenges in massive designs. He details the transition from architectural exploration to emulation and FPGA prototyping, highlighting how connected platforms help teams find power, performance, and DFT issues before tapeout. Chobisa argues that digital twins extend beyond electronics to model entire systems, such as automotive scenarios involving sensors and actuators, ensuring safety and reliability in complex products.

    Connected products & IoTDebugging & failure analysis
    6 chapters

  • · 24m

    Trust at Scale: The Next Challenge for AI-Driven Semiconductor Development

    Dr. Jim Shiely, Chief of Staff for Calibre Semiconductor R&D at Siemens EDA, discusses the 'trust bottleneck' emerging in semiconductor development as AI becomes embedded in engineering workflows. He explains that while AI accelerates tasks, it creates an overflow of data that requires 'trustworthy engineering intelligence'—grounded in independent verification tools like Calibre and physics-based models—to prevent hallucinations and ensure decision confidence. Shiely argues that successful companies will use digital twins and agentic flows to provide structured, traceable intelligence rather than just raw data, enabling humans to make rapid, confident decisions in a high-pressure environment.

    AI & machine learningChips, FPGAs & semiconductors
    7 chapters

  • · 1h 00m

    Do Spikes Need Common Language for Sensing and Learning?

    Dr. Sunny Bains chairs a panel at the University of Sussex featuring Dr. Chiara Bartolozzi (Italian Institute of Technology), Dr. Federico Corradi (Eindhoven University of Technology), Dr. James Knight (University of Sussex), Dr. Laura Kriener (Institute of Neuroinformatics, Zurich), and Dr. Sebastian Siegel (Peter Grünberg Institute, Jülich) to debate whether spikes provide a common language for sensing and learning. The panelists argue that while spikes offer energy efficiency, the field lacks a unified encoding scheme to integrate multimodal data, such as combining event-based vision with frame-based imaging or touch sensors. They discuss the commercial trajectory of neuromorphic engineering, highlighting early adoption in edge IoT and drones by companies like Okterov and Kinema, while emphasizing the need for co-design of hardware and algorithms to solve complex robotic tasks.

    Embedded firmwareSensors & measurement in products
    11 chapters

  • · 18m

    Designing the AI Factory from the Rack Up

    Neeraj Paliwal, Senior Vice President of Silicon IP Product Management at Synopsys, explains why the AI rack has replaced the data center as the fundamental unit of design and economics. He details the shift from 8kW to 130kW+ power densities in systems like NVIDIA's GB200 NVL72, driven by the need to optimize cost per token across distributed GPUs. Paliwal highlights that the industry is moving past single-die limits toward 3D stacking, custom HBM memory integration, and high-speed die-to-die interfaces like UCIe to overcome the memory and I/O bandwidth walls.

    Chips, FPGAs & semiconductorsBatteries & low-power design
    5 chapters

  • · 23m

    Beyond Automation: Self-Verifying AI Agents for Semiconductor and PCB Design

    Amit Gupta, Chief AI Strategy Officer at Siemens EDA, and Tim Costa, VP and GM of Industrial and Computational Engineering at NVIDIA, discuss their collaboration to build self-verifying AI agents for semiconductor and PCB design. They explain how the Fuse EDA AI agent uses NVIDIA's GPU-accelerated libraries and Nemotron models to execute long-running engineering workflows while verifying outputs against physics-based EDA engines. The speakers highlight a specific case study with NVIDIA's silicon team, where the Salido characterization suite achieved a 10x reduction in turnaround time and token costs by automating manual setup and error handling. They outline three milestones for the future: workflow coverage, verified repeatability, and integration into core engineering infrastructure, aiming to cut tape-out schedules in half.

    AI & machine learningChips, FPGAs & semiconductors
    9 chapters

  • · 58m

    Can Great Sky’s Light-Connected AI Enable Bigger Brains?

    Dr. Jeff Shainline, founder of Great Sky, explains how his company combines superconducting electronics with photonic interconnects to build neuromorphic AI hardware that operates at 4 Kelvin. He details the use of Josephson junctions, SQUIDs, and single-photon detectors to achieve extreme speed and energy efficiency, arguing that this architecture offers a 300x cost advantage for large language models compared to NVIDIA's GB 200 systems. The discussion covers manufacturing partnerships with companies like Seek and IMEC, the roadmap to wafer-scale integration, and the physical limits of cognition defined by light-speed communication.

    Mechanical engineeringAI & machine learning
    9 chapters

  • · 32m

    Under the Hood of Intelligent Systems

    Hezzy Sar, Executive Director of Product Management at Synopsys and Chair of the MIPI Alliance Board, discusses how the MIPI Alliance is evolving interface standards from mobile phones to automotive, industrial, and physical AI applications. He details specific protocols like MIPI AFI for automotive connectivity, CSI-2 features such as Smart Region of Interest (SROI) and Event Sensing and Processing (ESP), and DSI advancements for XR displays. The conversation highlights the importance of low-power, high-efficiency connectivity and the new security frameworks being integrated into these standards to ensure end-to-end protection for autonomous systems.

    Wireless & RFSensors & measurement in products
    7 chapters

  • · 46m

    Will Photonics Loosen the Bottlenecks in Neural Systems?

    Dr. Patty Stabile of Eindhoven University of Technology explains her all-optical neuromorphic computing architecture using semiconductor optical amplifiers and wavelength division multiplexing to achieve ultra-low latency processing without optical-to-electrical conversion. Hosts Dr. Sonny Baines and Dr. Juliette D’Angelo discuss the scalability, power efficiency, and material challenges of indium phosphide versus silicon photonics, while Professor Ralph Etienne-Cummings of Johns Hopkins University critiques the current state of the field and highlights applications in autonomous vehicles and 6G signal processing.

    AI & machine learningChips, FPGAs & semiconductors
    8 chapters

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