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Join Synopsys at booth 719 to learn about the broad range of AI capabilities in the ARC Embedded Processor IP portfolio. You’ll see demonstrations of emerging neural network architectures like transformers, the latest radar processing techniques, and vision/object detection for safety-critical automotive SoCs.
At our Booth #719 we will highlight the latest in practical technology to bring visual intelligence into embedded systems, mobile apps, cars, and PCs.
Please check back to this page as we update our event agenda.
Editor-in-Chief of Edge AI Vision Alliance, Brian Dipert, sat down with Gordon Cooper, Product Marketing Manager at Synopsys to give us a sneak peek at our ARC Processor Deep Dive on Thursday, May 19 and additional Synopsys event highlights. Click the image to see the interview video.
Thursday, May 19 | 12:00 PM – 3:00 PM PST
Santa Clara Convention Center | Room 203/204
11:30 AM - 12:00 PM | Check in & Lunch
12:00 PM - 2:15 PM | Speakers: Tom Michiels, ARC Processors System Architect, Pierre Paulin, Director of R&D
Topics:
2:30 PM - 3:30 PM | Demos, snacks, networking in common area
AI applications — including automotive vision and lidar, digital still cameras, surveillance, smartphones — are driving the need for more efficient neural network processing. The trick is getting GPU-level performance within an embedded power and cost budget. This session covers Synopsys’ new Neural Processing Units (NPUs) IP based on a novel architecture and trusted software tools which significantly improve hardware utilization, support the latest neural network trends and scale from battery-powered devices to L3-L5 autonomous driving. We will present inference benchmarks showing performance and power comparisons versus leading GPUs. We will also introduce the evolution of our MetaWare software toolkit to accelerate time to market, and we’ll demonstrate this new NPU solution.
Wednesday, May 18 | 10:15 AM – 10:45 AM PST
The neural network architectures used in embedded real-time applications are evolving quickly. Transformers are a leading deep learning approach for natural language processing and other time-dependent, series data applications. Now, transformer-based deep learning network architectures are also being applied to vision applications with state-of-the-art results compared to CNN-based solutions. In this presentation, we will introduce transformers and contrast them with the CNNs commonly used for vision tasks today. We will examine the key features of transformer model architectures and show performance comparisons between transformers and CNNs. We will conclude the presentation with insights on why we think transformers are an important approach for future visual perception tasks.
Thursday, May 19 | 12:00 PM – 3:00 PM PST
Time to meet with Synopsys executives and technical experts.
Demo 1: Executing Transformer Neural Networks in ARC NPX6 NPU IP
Demo 2: Driver Management System on ARC EV Processor IP with Visidon
Demo 3: Neural Network-Enhanced Radar Processing on ARC VPX5 DSP with SensorCortek