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Tuesday, October 16
9:00am DSP Software Development Masterclass with Arm and MathWorks 9:00AM - 9:50AM Ram Cherukuri, MathWorks; Salman Arif, Arm show more Description Our world is increasingly digital and intelligent, with homes becoming smarter and devices becoming more human-like. That means that high-performance, multi-sensor devices such as wearables, audio headsets, smart speakers and more, all require embedded developers to incorporate a level of digital signal processing (DSP). Furthermore, the application of machine learning techniques to signal processing presents new opportunities and challenges for embedded developers. This joint talk by Arm and MathWorks will demonstrate how embedded developers can take advantage of faster prototyping and faster time to production with signal processing instructions, software libraries, tools and resources available for Cortex-M processors. In addition, this presentation will showcase implementation examples using machine learning (ML) for pattern recognition in audio applications. Time Tuesday, Oct 16 9:00AM - 9:50AM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210H
11:30am Practical Challenges and Proposed Solutions for Implementing Embedded AI on Arm Cortex A/M Devices 11:30AM - 12:20PM Ganesh Balamitran, Renesas Electronics show more Description With the rapid growth in AI and machine vision, the industry’s focus is quickly turning to deploying neural networks for inference on end-point devices, like Arm Cortex A microprocessors (MPUs) and even Cortex M MPUs. In this session, we will share our learnings on the challenges of using 1) various implementations of popular networks on full frameworks like TensorFlow and Caffe, as well as 2) customized/optimized code better suited for embedded MPUs. We will also explore some of the new tool approaches and new tools that are addressing these challenges. Time Tuesday, Oct 16 11:30AM - 12:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210E
1:30pm Designing Intelligent Systems Using Resource Constrained Edge Devices 1:30PM - 2:20PM Jacob Beningo, Beningo Embedded Group show more Description Traditional embedded software engineers often think that machine learning and intelligent systems are outside the realm of microcontroller-based systems and, therefore, outside their realm of expertise. Advances in microcontroller technology have made designing intelligent systems using these resource-constrained devices a reality. In this session, we will examine the tools and capabilities that are available to microcontroller designers to start using machine learning and adding a new level of intelligence to their devices. Developers will walk away understanding that machine learning and AI are not just for big data and the cloud. Time Tuesday, Oct 16 1:30PM - 2:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210F SmartNICs: Giving Rise To Smarter Offload at The Edge and In The Data Center 1:30PM - 2:20PM Jeff Defilippi, Arm show more Description Distributed cloud and network infrastructures, the fundamental building block in our hyper-connected world, rely upon heterogeneous computing to efficiently analyze and service vast amounts of real-time data at its source with standard virtualization and container software stacks. This talk will discuss the evolution of smart acceleration solutions, emerging system requirements, and detail how Arm processors, system IP, custom accelerators, and architectures, such as AMBA, PCIe, and CCIX, can be combined to customize accelerated compute solutions for hyperscale performance at the power constrained edge. Time Tuesday, Oct 16 1:30PM - 2:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210H
3:30pm There Is No One-Size-Fits-All in Machine Learning at the Edge 3:30PM - 4:20PM Jim McGregor, TIRIAS Research show more Description Many silicon and system architectures are emerging for edge computing. These solutions vary from using standard logic solutions to dedicated neural processing units (NPUs) and in-memory processing units. While all will work as inference engines, there are tradeoffs between performance, power consumption, manufacturing complexity, cost, and form factor size. The choice is also dependent upon the machine learning task(s) to be performed. As a result, the software model has a significant impact on the choice of machine learning solution. This presentation will discuss the different approaches and the most appropriate use by application and system requirements. Time Tuesday, Oct 16 3:30PM - 4:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210G
4:30pm How Do I Select IP to Use for My Machine Learning System?  4:30PM - 5:20PM Helena Zheng, Arm show more Description Machine learning (ML) processing requirements vary significantly according to the network and workload; there is no “one-size-fits-all” solution. Examining use cases, workloads, and performance data from real networks, this talk will give examples to help you choose the right Project Trillium IP from Arm for your application. Examples will include MCUs for cost- and power-constrained embedded IoT systems through CPUs for moderate performance with general-purpose programmability. Other examples include GPUs for faster performance with graphics-intensive applications to NPUs, such as with the Arm ML processor, for intensive ML processing, giving the highest available performance and efficiency.    Time Tuesday, Oct 16 4:30PM - 5:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210G
Wednesday, October 17
9:00am CCIX: Seamless Data Movement for Accelerated Applications 9:00AM - 9:50AM Jon Masters, Red Hat; Millind Mittal, Xilinx; Jeff Defilippi, Arm show more Description During Arm TechCon 2017, members of the CCIX consortium provided an introduction to the chip-to-chip interconnect architecture, which was created to solve the performance and efficiency challenges of emerging acceleration applications, such as machine learning, network processing, storage/memory expansion, and analytics that combine processing and acceleration. At this year’s talk, CCIX members will not only focus the advancements to the hardware and software architecture. This talk will also detail the use cases that benefit from the cache coherent, shared virtual memory paradigm and seamless data movement between processors and accelerators, including FPGAs, GPUs, network/storage adapters, intelligent networks, and custom ASICs. Time Wednesday, Oct 17 9:00AM - 9:50AM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210G Optimizing IoT End Nodes for Machine-Learning 9:00AM - 9:50AM Shawn Prestridge, IAR Systems AB show more Description This session will explore the challenges of coding and debugging complex machine-learning (ML) and artificial intelligence (AI) systems. We will cover how to boost code performance and how to use the advanced debugging and trace capabilities in conjunction with the Neural Network Kernels for Arm Cortex-M software library (CMSIS-NN). We will also look at why code quality is such a major issue with machine learning and how you can future-proof your source code. This session is a must-attend for embedded developers that want to improve their development processes for ML and AI in automotive, industrial, medical, and other applications. Time Wednesday, Oct 17 9:00AM - 9:50AM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210E
1:30pm Machine Learning on Arm Cortex-M Microcontrollers 1:30PM - 3:10PM Alessandro Grande, Arm; Mark Woods, Arm show more Description Deep learning algorithms are gaining popularity in IoT-edge devices due to their human-level accuracy in many tasks, including image classification and speech recognition. This creates increasing interest in deploying neural networks (NNs) on low-power processors found in always-on systems, such as those based on Arm Cortex-M microcontrollers. CMSIS-NN is a library of optimized software kernels for running NNs on Cortex-M cores. In this hands-on workshop, we will show developers how to run image classification on Cortex-M microcontrollers using CMSIS-NN. Bring your own laptop to participate in our hands-on workshop!        First 50 attendees will receive an OpenMV Cam M7 microcontroller board and free 90-day Keil MDK Professional development suite. Audience Level: Intermediate Time Wednesday, Oct 17 1:30PM - 3:10PM Session Type Conference Workshop Pass Type All-Access Pass,Arm Mbed Connect Pass,Expo Floor Pass Location Grand Ballroom 220C Optimized Edge Cloud Use Case with Arm-based uCPE 1:30PM - 2:20PM Kshitij Sudan, Arm; Alex Trigoub, Telco Systems show more Description Universal CPE (uCPE) is one of the first commercial edge computing use cases. A key challenge is effective deployment of multiple VNFs on a cost and power-efficient device. We will discuss utilizing Arm TrustZone to enhance device and network security with HW-based isolation of OS trusted firmware and VNFs. The talk will discuss the software and SoC aspects and advantages of creating an Arm-based uCPE solution. Time Wednesday, Oct 17 1:30PM - 2:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210E
2:30pm The Arm Ecosystem - Driving Performance Beyond Moore’s Law - Sponsored By Arm 2:30PM - 3:20PM Lionel Belnet, Arm; Miguel Nunes, Qualcomm show more Description CPU performance and efficiency from Arm has driven significant technological advances with our ecosystem partners creating platforms that take the user experience to new heights. In this talk you will see how Arm and Qualcomm are now enabling what others have struggled to do from small screens to large. Get insights into the roadmap, CPU performance and a deep dive into the latest Arm CPU from the product experts from both companies. Time Wednesday, Oct 17 2:30PM - 3:20PM Session Type Sponsored Session Pass Type All-Access Pass,Expo Floor Pass Location Meeting Room 211A/C
3:30pm Accelerating & Optimizing Machine Learning on Arm Cortex-A 3:30PM - 5:10PM Jason Andrews, Arm; George Lander, Arm show more Description There has been an explosion of interest around deploying deep learning enabled applications on embedded platforms, in everything from digital assistants to home entertainment systems. Arm NN bridges the gap between existing machine learning frameworks and the underlying hardware. It enables translation of existing frameworks, such as TensorFlow and Caffe, allowing them to run efficiently, without modification, across a variety of Arm Cortex CPUs and Arm Mali GPUs. In this workshop we will show developers how to get up and running with Arm NN on Linux and how to analyze application performance with Arm’s profiling tool, Streamline. Bring your own laptop to participate in our hands-on workshop! First 40 attendees will receive a HiKey 960 development board & free 60-day Streamline Performance Analyzer with Arm DS-5 Ultimate Edition. Audience Level: Intermediate Time Wednesday, Oct 17 3:30PM - 5:10PM Session Type Conference Workshop Pass Type All-Access Pass,Arm Mbed Connect Pass,Expo Floor Pass Location Grand Ballroom 220C Computer Vision at the Edge and in the Cloud: Understanding the Tradeoffs 3:30PM - 4:20PM Jeff Bier, Embedded Vision Alliance show more Description Computer vision is rapidly becoming ubiquitous. From devices that prevent automobile accidents, to smart cameras that measure the flow of customers in stores, as well as home assistants that monitor the health of elders, vision is showing up everywhere. A key architectural choice underlies this ubiquity: should vision processing be done at the edge, in the cloud, or a combination of the two? In this presentation, Jeff Bier, founder of the Embedded Vision Alliance, will discuss the benefits and trade-offs of edge, cloud, and hybrid approaches, and when you should consider each option. Time Wednesday, Oct 17 3:30PM - 4:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210A Speed Up Your AI Designs with Dedicated Arm Machine Learning Hardware 3:30PM - 4:20PM Ian Forsyth, Arm show more Description Discover the features and benefits of Arm’s Project Trillium's hardware processors: Machine Learning (ML) and Object Detection (OD) processors, their software support, and applicability for different markets and the options for incorporating them in differentiating SoC designs. This talk will describe our strategy and plans for the highly scalable, ground-up designed ML architecture, the markets it will target, and future product iterations. It will also include a comparison with other Arm solutions, enabling you to choose the best software and hardware combination to address your specific needs.   Time Wednesday, Oct 17 3:30PM - 4:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210E
4:30pm Low Power Neural Networks on Cortex-M7 CPUs Using the OpenMV Cam H7 4:30PM - 5:20PM Kwabena Agyeman, OpenMV, LLC show more Description In March 2018, Arm released the Arm CMSIS NN library which lets you run neural networks trained with desktop tools like Caffe and TensorFlow on low power Cortex-M7 microcontrollers for edge device IoT computing. With the OpenMV Cam H7 powered by a 400 MHz Cortex-M7 STM32H743VIT6 chip, we demonstrate how to turn desktop NNs into binary files that can be dynamically loaded and run on images. Time Wednesday, Oct 17 4:30PM - 5:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210H
Thursday, October 18
9:00am A Balanced Approach to Inference Implementation at the Edge 9:00AM - 9:50AM Markus Levy, NXP Semiconductors show more Description The IoT led to an explosion of data. Though it was once thought the cloud would handle this data, the industry soon realized this was impractical. As a result, machine learning on the edge has increased in popularity and usefulness. One of the most fascinating things about machine learning, or edge computing, is that it can be accomplished on a wide range of device categories, from MCUs with Arm Cortex-M4 and M7 cores to complex SoCs with high-end A-class cores, GPUs, and ML accelerators. This presentation describes the cost and performance tradeoffs for these device categories, using Vision, Voice, and Vibration as examples. Time Thursday, Oct 18 9:00AM - 9:50AM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210E
11:30am Enabling Real-time Machine Vision and Deep Learning on Small Devices 11:30AM - 12:20PM Laurent Itti, JeVois Inc show more Description The next generation of consumer devices will require more natural interfaces, stronger situation awareness, and better perceptual capabilities, both to inform users and to interact with them. Computer vision and deep learning are crucial enablers, yet they have remained difficult to implement on embedded systems. I will survey machine vision and deep learning frameworks and technologies available for deployment today, and demonstrate real-time implementations on $5 Arm Cortex-A7 processors of algorithms ranging from barcode reading, to face recognition, and object recognition using deep neural networks. Time Thursday, Oct 18 11:30AM - 12:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210G
1:30pm Arm-Synopsys Collaboration to Enable Edge-to-Cloud Computing 1:30PM - 2:20PM Rahul Deokar, Synopsys, Inc.; David Koenen, Arm show more Description Arm and Synopsys are collaborating to enable rapid SoC innovation from hyperscale data centers and HPC all the way out to the network and provider edge. These all demand high-performance, efficient, scalable, heterogenous compute solutions. Learn how Arm Cortex-A processors, Arm CoreLink Coherent Mesh Network and System Guidance for Infrastructure (SGI), provide a scalable, energy-efficient solution and, in combination with Synopsys’s solutions (Design Platform with Fusion Technology, QuickStart Implementation Kits (QIKs), Verification Continuum Platform, and DesignWare Interface IP), enable designers to speed creation of their infrastructure products from power-constrained to peak performance on a common software platform. Time Thursday, Oct 18 1:30PM - 2:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210F How to Build Optimized ML Applications with Arm Software - Sponsored By Arm 1:30PM - 2:20PM Robert Elliott, Arm show more Description Discover how developers are using the Compute Library, CMSIS-NN and Arm NN to develop AI applications targeting CPUs, GPUs and new processor architectures for Machine Learning (ML). After a brief overview of Arm’s ML software offerings, the talk will examine real-world use cases and how the ARM ML software libraries and APIs may be leveraged to create highly optimized ML applications for platforms ranging from Cortex-M microcontrollers to Cortex-A CPUs, Mali GPUs and the new Arm ML Processor (Marvin). To conclude, the talk will review the roadmap for Arm NN and related low-level software libraries, while looking at some of the challenges ML presents in low-power environments. Time Thursday, Oct 18 1:30PM - 2:20PM Session Type Sponsored Session Pass Type All-Access Pass,Expo Floor Pass Location Meeting Room 211A/C
2:30pm Preserving Proprietary Innovation in an Open Source World 2:30PM - 3:20PM Mark Orvek, Linaro; Tyler Baker, Foundries.io; Jeff Kaufman, Red Hat; Iliyan Malchev, Google; Hervé Sibert, GlobalPlatform.org show more Description Uniquely in the Arm ecosystem, there is the collision of two worlds: the well-established embedded world of often bespoke platforms, stacks, and tools, and the arriving world of open-source software frameworks, often originating on commodity hardware. This session looks at this collision and asks if this meeting of open source and proprietary innovation has to be a zero-sum game. Time Thursday, Oct 18 2:30PM - 3:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210E Securely connecting MCUs to the Cloud 2:30PM - 3:20PM Richard Elberger, Amazon Web Services show more Description This talk will outline the challenges of embedded development and highlight how Amazon FreeRTOS solves these challenges. The Amazon FreeRTOS discussion will center around differentiated features such as tickless mode for low power consumption, and the ecosystem of tools available for development, test, and debug. We will also discuss use cases and relating microcontroller architecture choices. Time Thursday, Oct 18 2:30PM - 3:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210B
3:30pm Advances in AR and VR with Arm based Technologies - Sponsored By Arm 3:30PM - 4:20PM Sylwester Bala, Arm show more Description Recent advances in AR and VR have led to some exciting developments in use cases and applications based on Arm technology. In particular Augmented Reality enables a wide range of new use cases and new businesses opportunities in the mobile segment. In this session you will learn about the key Arm based technologies and solutions for Augmented Reality using SLAM (Simultaneous Location and Mapping) as a case study. SLAM is the basis for use cases that range from tracking camera pose in mobile AR and VR to more complex high-level understanding of the real world seen through a camera. It is in the heart of AR headsets, AR smartphones and solutions for self-driving cars, unmanned drones, planetary rovers and a lot more other use cases. You will learn about the importance and challenges associated with the technology on current and future platforms. An insight on the system pipeline and what to take into consideration while building AR and VR solutions to achieve best in class end user experience. Time Thursday, Oct 18 3:30PM - 4:20PM Session Type Sponsored Session Pass Type All-Access Pass,Expo Floor Pass Location Meeting Room 211A/C Analyzing Machine Learning Inference with Arm Performance Tools 3:30PM - 4:20PM Stephen Barton, Arm show more Description Machine learning workloads can be complex for performance analysis and tuning, especially when the workload utilizes a mix of CPU, GPU and dedicated accelerators within the same system. In this talk, we will describe the performance analysis challenges faced by the different classes of ML users, and show how Arm has extended its performance analysis tools with a deep understanding of the ArmNN framework and ML processor. These new capabilities make it possible to right-size your machine learning algorithms for the chosen hardware platform, or to verify that a platform’s hardware capabilities are being exploited to the fullest extent possible. Time Thursday, Oct 18 3:30PM - 4:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210H
4:30pm Building Edge Applications on Microcontrollers 4:30PM - 5:20PM Cheng-Fu Tan, Arm show more Description Many have believed that machine learning was computationally infeasible on microcontrollers (MCUs). Recent works in algorithm and software engineering have reduced the computational requirement for deep learning significantly. This enables deep learning models to be deployed on affordable and simple systems, the MCUs. Intelligence on the edge will allow us to build smarter devices and serve new applications. In this talk, we will look at how to train a simple model in Tensorflow, then use uTensor and CMSIS-NN to deploy it on Mbed. Time Thursday, Oct 18 4:30PM - 5:20PM Session Type Conference Session Pass Type All-Access Pass Location Executive Ballroom 210G
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