|Tuesday, September 11, 2018 8:30 a.m. - 6:30 p.m. PDT|
|DoubleTree by Hilton Hotel
2050 Gateway Place, San Jose, CA 95110
This free one-day event consists of multiple tracks in which Synopsys experts, ecosystem partners and the ARC user community will deliver technical presentations on a range of topics, including, artificial intelligence (AI), automotive safety, internet of things (IoT), embedded vision and much, much more.
Come and learn about the latest technologies and trends in embedded processor IP, software, programming tools and applications.
|8:30-9:00am||Check-in and Breakfast||9:00-9:15am||Welcome & Opening Remarks||9:15-10:15am||Keynote: Life on the “Edge”
Satyen Yadav, General Manager, IoT Ecosystem Development, Amazon Web Services
|10:30-11:15am||The Marriage of AI and Safety in Automotive SoCs - Synopsys||Addressing the Challenges of Always-on IoT with Efficient Processors for Machine Learning - Synopsys||Using Trace Visualization for Efficient Debugging of Embedded Systems - Percepio|
|11:15am-12:00pm||Reducing the Total Cost of Ownership with Classic and Adaptive AUTOSAR - Elektrobit||Implementing Artificial Intelligence in Embedded Vision, IoT, and Smart Home Applications - Synopsys||High-Performance Solutions for Next-Generation SSD Designs - Synopsys|
|1:00pm-1:45pm||Personalize the In-Cabin Experience with Face Recognition and Inference of Driver Emotional States - PathPartner||Fast and Ultra-Low Power Graphics Development for Mobile & Embedded Systems - Think Silicon||Easing Complex Application Development with Processor and System Trace Resources - Synopsys|
|1:45pm-2:30pm||Taming AI Using Convolutional Neural Networks with Compression and Pruning - Synopsys||Securing Mobile IoT from Chip to Cloud with Integrated SIM Solutions - Truphone||Enabling Ultra-High Performance, Low-Power 5G Modem Designs with Heterogeneous Multicore Systems - Synopsys|
|2:30pm–3:15pm||Optimizing Deep Learning Perception Software for ADAS and Autonomous Driving - StradVision||Streaming Low-Power Audio to "Hearable" Devices Using Bluetooth 5 - Synopsys||Accelerating Group Theoretic Cryptography with ARC APEX Instructions - SecureRF|
|3:30pm-4:15pm||Functional Safety Certification - Your Advantage - SGS-TÜV||Deploying NB-IoT Communication Solutions with Extensible Processors - Synopsys||Building an Embedded Vision Application with a Caffe CNN Model and OpenVX - Synopsys|
|4:15pm–5:00pm||Extending Control and DSP Performance for Automotive RADAR Applications - Synopsys||Reducing Dynamic Power and Time-to-Tapeout for High-Performance AI Processor SoCs - Synopsys||Accelerating AI/Neural Network Performance While Reducing Power in Android Devices - Synopsys|
|5:00pm-6:30pm||Demos & Networking Reception|
Today, an increasing number of objects are being connected to the internet at an unprecedented rate. As a result, customers are collecting vast amounts of valuable IoT data that was previously unavailable. We are already seeing the benefits of applying machine learning models to process data at the source where it is being generated- farmers predict crop yield, power companies predict energy demand, vehicles can identify distracted drivers, and doctors deliver improved care with real-time insight from medical devices … the possibilities for applying intelligence at the edge are countless. However, processing and analyzing this vast amount of IoT data is not possible with the help of traditional business intelligence tools. In this session, we will showcase how customers can use AWS’s IoT, artificial intelligence, and machine learning services to gain predictive insights and take intelligent, real-time actions on their IoT data, from the cloud to the edge.
As the automotive industry looks beyond Level 2 (Driver Assist) designs, the race is on to deliver high-performance safety-critical autonomous vehicle components powered by the latest AI technology. AI techniques can provide increased accuracy for object and pedestrian detection, but these designs must still meet the ISO 26262 standard’s most stringent level of functional safety and fault coverage. In this presentation, we analyze an autonomous driving use-case emphasizing the need for the inseparable union of AI and safety. We will discuss how Synopsys achieves this marriage without significant impact on performance, power, or area compared to non-ASIL Ready processors. From an understanding of this use-case and requirements, we present an embedded SoC solution providing the highest level of safety without compromising AI performance.
From architecture through to tape-out, we provide an overview of the design, verification, and safety methodologies required for SoC safety certification.
In this talk, we cover the key features of Classic and Adaptive AUTOSAR and how these features reduce the total cost of ownership. Participants should expect to walk away with a high-level overview of AUTOSAR and an understanding of how it provides value for their current and future automotive projects. We focus on the design, configuration, security, and safety aspects that provide the hallmark flexibility of the AUTOSAR platform. Finally, we discuss migration and reuse strategies that match the changing E/E architecture seen within the vehicles.
Cars are more than just transportation – they provide entertainment, communication, convenience, and more. The ideal in-cabin experience is personalized to each occupant in the car to enhance aesthetics and entertainment as well as safety and automotive performance. Offering personalized experiences gives car manufacturers a way to differentiate their products from other options in the market. In this presentation, we will discuss how designers and system architects can enhance in-cabin personalization with face recognition, micro radar, iris scanning, and other sensor fusion technologies to implement, for example, driver drowsiness alerts. We will explain how driver and passenger recognition can be the key enabler for personalization targets, and how to provide this functionality with limited impact on power and area. Finally, we will explore the types of CNN networks available for recognizing actions/expressions and how recognition can provide semantic context.
To implement AI applications at the edge, you need to move from training to inference on your embedded target. This transition comes with a new set of considerations to take into account. For the target system, you must consider factors such as performance, memory size, throughput, and bandwidth, as well as maintaining the accuracy of your graph. In this session, we will show how you can profile your graph and then achieve your design targets with techniques such as feature-map compression, graph acceleration and coefficient pruning.
StradVision has developed SVNet, a deep-learning-based object detection of 6 object classes (Pedestrian, Car, Bus, Truck, Box Truck, Two-wheeler). SVNet is robust for bad weather/lighting conditions (e.g., rain, snow, fog, night), small object sizes (e.g., 32 pixel height pedestrian, 20 pixel height vehicle), and occlusion (up to 75% occlusion). In this presentation, I will describe automotive OEM/Tier-1’s requirements (for frontal camera, rear camera, around view monitoring), technical challenges, and how our solution can address them with Synopsys EV6x Vision Processors and its dedicated CNN Engine.
The ISO 26262 standard does not require a certification of processes or products. But, certifications can ease your development cycle. In this presentation, we will describe the advantages of process, product and personal certifications using Synopsys processes and products as examples. We will also show how providing certificates to customers means offering them valuable additional benefits.
This session will cover object detection and classification techniques using the 77GHz RADAR signal source for automotive advanced driver-assistance system (ADAS) applications. An overview of the RADAR technology used in ADAS applications, including review of the relevant signal processing, typical requirements and design parameters will be given, and some of the design considerations and trade-offs of implementing the RADAR system will be discussed. We will close with and an example of an efficient RADAR signal processing chain (3D, FFT, CFAR, clustering and tracking) algorithm implemented on the ARC HS47D processor, where processing on the ARC DSP core is the forming a data cube (3D FFT) to find range, relative velocity, and direction of arrival (DoA) of objects. In this example, front-end FFT processing is implemented on dedicated hardware blocks, performing 3-stage FFT computation.
The ARC EM DSP processors are extremely well-suited for performing machine learning inference in always-on IoT devices. Smart IoT devices increasingly offer always-on features to allow advanced user control. For example, they can be always-listening to allow control through voice commands, or always-watching to support system wake-up by means of a face trigger. Such always-on functions often employ machine learning techniques for recognizing voice commands, faces, etc. A key requirement for implementing such functions is a very low power consumption, as battery lifetime is key for the always-on capability. In this presentation we discuss the key features of the ARC EM DSP processors that enable efficient machine learning inference. We then show how excellent results, in terms of low MHz requirements, small code size and low power consumption, are achieved for voice trigger and face trigger applications.
As neural network techniques are applied to consumer applications, designers must figure out how to introduce these computationally demanding algorithms while minimizing power consumption. This presentation will discuss how to balance the tradeoffs between performance, power, area, and bandwidth in AI applications. It will cover the evolution of CNN graphs, and describe the attributes of popular graphs such as Masknet, ICNET, and RetinaNet for IoT and smart home applications. Finally, it will touch on how an embedded vision processor architecture can maximize computational efficiency without sacrificing accuracy, using facial recognition for the IoT as an example.
High quality CGI (Computer Graphics) and high-resolution display support is proliferating dramatically in embedded, automotive, wearable and IoT devices.
NEMA®|t200 is the latest member of the NEMA|GPU-Family and is a perfect candidate, in combination with ARC Processors, for the acceleration of OpenGL|ES / OpenVG graphics content, providing very high graphics performance even in memory and power resource limited applications. In addition to the GPU, Think Silicon’s focus is to provide a comprehensive SDK including NEMA|Power-Profiler and NEMA|GUI-Builder to support and help developers to optimize their applications for performance and power and to assist them in rapid system deployment. In this presentation Think Silicon will demonstrate the strength of a fully-fledged development system, from RTL implementation to GUI (Graphical User Interface) creation.
The SIM card as we know it is disappearing. This session explores how integrating SIM functionality into SoC designs can greatly simplify how mobile IoT connectivity is enabled, secured, and managed. Truphone will demonstrate how its eSIM solution running on an ARC-based security platform, combined with its global connectivity and remote SIM provisioning services is enabling secure, out of the box connectivity from chip to cloud.
Wireless audio solutions have become pervasive, however, battery life remains a key limitation. The implementation of a standardized audio solution over Bluetooth will revolutionize the adoption and use of hearables not only because of the battery life improvement but because of the new applications Bluetooth Low Energy audio will enable. We will explore the new Low Complexity Communication Codec (LC3), the required IP components, and how those IP components are being optimized for next-generation Bluetooth audio hardware for SoC integration.
NB-IoT is an emerging technology for narrow-band wireless communication standardized by the 3GPP. It was designed with a focus on minimizing the end-user equipment power and performance requirements to enable the widespread deployment of NB-IoT compatible devices and ensure quick technology adoption. It created a market for licensable software-defined IP and increased the demand for efficient and flexible IP cores to execute this software. This presentation highlights the key challenges of NB-IoT modem design and proposes an optimization strategy demonstrating the efficiency of Synopsys ARC EMxD family of cores as a platform for software-defined NB-IoT modems.
Developing new AI applications requires implementing cutting-edge technologies while meeting performance, power, area and time-to-market requirements. Edge applications require low power, while CNN engines can be especially challenging to a design’s power budget due to the density of the multiply-accumulates needed to run large neural network computations. In addition, designers can face tedious and time-consuming iterations for floor planning and routing in order to meet PPA targets. This presentation will describe how usage of specialized logic cells and memories can address specific RTL-to-GDSII implementation challenges for CNN engines while reducing time-to-tapeout with optimal PPA. We will show a case study describing how utilizing the HPC Design Kit of logic libraries and embedded memories optimized for the Synopsys EV61 Embedded Vision Processor resulted in lower power and faster design closure.
Software issues related to timing or resource usage can be difficult since they are not directly visible in the source code, but rather are an emerging property of the system. Such issues call for software tracing. Developers often associate tracing with instruction tracing and overwhelming amounts of low-level information, where you can’t see the forest for all the trees. However, recent advances in trace visualization and software-generated tracing offers an alternative approach that is more suitable for finding anomalies in complex software behavior. This presentation introduces software tracing and demonstrates the potential of state-of-the-art trace visualization, using Percepio Tracealyzer as an example.
Storage is a critical component of the technology enabling online business, information access, streaming video, artificial intelligence and much more. Most of the electronics that we use today wouldn’t be possible without the ever-increasing size and performance that we are seeing from flash storage. This increasing capacity and performance will challenge current methods of maintaining and using data in storage mediums. This is leading to an interest in using artificial intelligence to enable software to dynamically balance and optimize data on SSDs to maximize capacity and throughput. This presentation will investigate how machine learning and other techniques will be used on future SSD designs, and the underlying software and hardware that will make it possible.
Complex application development is made easier by integrating trace and debug systems that enable observability and the ability to debug early in the design phase, minimizing bug discovery impact, by focusing development resources. This presentation demonstrates the flexibility and breadth of resources provided by ARC Trace Solutions for advanced processor and system development.
The 5G standard pushes the requirements on wireless communications equipment for greater than 1Gbps data rates as well as reducing system latency, allowing an expansion of 5G use cases to automotive and other timing-critical applications. SoC modem developers for 4G systems previously met performance requirements with heterogeneous systems, using multiple task-specific processor cores. 5G modem SoCs for user equipment (mobile devices) will need to take the heterogeneous implementation further to provide greater performance for higher data rates, larger MIMO configurations and lower latency, while maintaining similar power budgets to 4G modems. This session will go through the range of digital signal processors, controller cores and task-specific cores that will allow 5G modem SoC developers to implement the required amount of programmability/flexibility in their design, while achieving the performance and low power requirements.
This presentation will describe how a mathematically efficient cryptographic operation is significantly sped up using the ARC Processor EXtension (APEX) technology. We will give a brief overview of the underlying math operations and how they are implemented in software only. Then we will outline our approach at offloading the most compute-intensive operations onto hardware together with the design process we followed using Synopsys ARChitect, MetaWare, and Intel Quartus Prime. Attendees will see a comparison of the resulting performance metrics with APEX versus an assembly language-only implementation.
The design example will use a fast, small-footprint, and low energy digital signature algorithm that has immediate applicability for a wide range of IoT solutions and is ideally suited for applications where an ARC processor may need to securely communicate with an 8- or 16-bit device. Attendees will learn how to incorporate this and other security methods into their own ARC-based designs.
You’ve picked your CNN graph and embedded vision processor – now what? In this tutorial, we will walk you through the tool flow required to take your concept to reality, using a Caffe CNN graph and OpenVX kernels with MetaWare EV Development Toolkit, the software development environment for Synopsys EV6x processors. We will explain how to use OpenVX to create an image processing graph in conjunction with the CNN engine, as well as covering best practices for optimizing your application for an embedded environment.
With Android 8.1, Google has added the Neural Network API to the Android mobile operating system. This API is designed to ease offloading neural network workloads to accelerators such as the GPUs, DSPs and other optimized processors such as the Synopsys EV6x embedded vision processor. In this presentation, we will show how the Android Neural Network API, TensorFlow Lite and the EV6x processor work together to increase neural network performance while reducing power consumption. Power and performance comparisons between running on an application processor versus the benefits of offloading computations to the EV6x processor will be discussed.
Tracealyzer is the premier trace visualization tool for developers of embedded software systems. Tracealyzer supports several leading real-time operating systems such as Amazon FreeRTOS and ThreadX. In collaboration with Synopsys, we now introduce Tracealyzer support for profiling of OpenVX applications on Synopsys EV6x vision/AI processors.
This demo highlights the performance advantage achieved by accelerating certain operations with APEX. The demo compares the time it takes (in processor cycles) to verify a digital signature on an ARC EM4 with and without APEX acceleration.
StradVision has developed SVNet, a deep-learning-based object detection of 6 object classes (Pedestrian, Car, Bus, Truck, Box Truck, Two-wheeler). SVNet is robust for bad weather/lighting conditions (e.g., rain, snow, fog, night), small object sizes (e.g., 32 pixel height pedestrian, 20 pixel height vehicle), and occlusion (up to 75% occlusion).
Synopsys and Think Silicon developed a prototype sporting a Synpopsys ARC EM5D Processor with a NEMA®- 3D GPU including NEMA®|GFX-API, 9D-Sensor, 5” TFT LCD and is fully battery powered. The solution is aimed for developers to rapidly implement high-quality 3D graphics in connected ultra-low-power wearables and embedded devices with reduced risk and cost.
In this demo, Tortuga Logic will present the results of security analysis performed on the ARC processor using their security simulation software Unison. Several security features are tested, with results displayed in the Unison analysis platform.
A demonstration of Truphone’s integrated SIM solution running on the ARC EM Starter Kit board integrated a with a commercial mobile device. It demonstrates the main use-cases of GSMA Remote SIM provisioning for IoT and consumer devices. Using Truphone bootstrap connectivity it will show how in-the-field remote device activation becomes possible for any mobile operator and can completely remove the logistics and costs associated with physical SIM cards.
See how the Synopsys EV6x Embedded Vision Processor with deep learning can offload application processor tasks to increase performance and reduce power consumption, using an Android Neural Network API example.
This demo shows how to integrate Bluetooth 5 and Bluetooth Mesh capabilities into your IoT SoCs, and get a jump start on your next design with industry proven open source software. The demonstration features a complete solution containing Synopsys Bluetooth Low Energy PHY and Link Layer IP and Synopsys ARC EM Processor.
The Synopsys EV6x Embedded Vision Processor’s vector DSP and CNN engine combine deep learning with traditional computer vision. The demo shows how the tightly-integrated CNN engine executes deep learning inference (using TinyYOLO) along with the vector processor, which tracks points in the image using sparse optical flow algorithms.