January Circuit Cellar: Sneak Preview

Happy New Years! The January issue of Circuit Cellar magazine is coming soon. Don’t miss this first issue of Circuit Cellar’s 2019 year. Enjoy pages and pages of great, in-depth embedded electronics articles produced and collected for you to enjoy.

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Here’s a sneak preview of January 2019 Circuit Cellar:

TRENDS & CHOICES IN EMBEDDED COMPUTING

Comms and Control for Drones
Consumer and commercial drones represent one of the most dynamic areas of embedded design today. Chip, board and system suppliers are offering improved ways for drones to do more processing on board the drone, while also providing solutions for implementing the control and communication subsystems in drones. This article by Circuit Cellar’s Editor-in-Chief Jeff Child looks at the technology and products available today that are advancing the capabilities of today’s drones.

Choosing an MPU/MCU for Industrial Design
As MCU performance and functionality improve, the traditional boundaries between MCUs and microprocessor units (MPUs) have become less clear. In this article, Microchip Technology’s Jacko Wilbrink examines the changing landscape in MPU vs. MCU capabilities, OS implications and the specifics of new SiP and SOM approaches for simplifying higher-performance computing requirements in industrial applications.

Product Focus: COM Express Boards
The COM Express architecture has found a solid and growing foothold in embedded systems. COM Express boards provide a complete computing core that can be upgraded when needed, leaving the application-specific I/O on the baseboard. This Product Focus section updates readers on this technology and provides a product album of representative COM Express products.

MICROCONTROLLERS ARE DOING EVERYTHING

Connecting USB to Simple MCUs
Sometimes you want to connect a USB device such as a flash drive to a simple microcontroller. Problem is most MCUs cannot function as a USB host. In this article, Stuart Ball steps through the technology and device choices that solve this challenge. He also puts the idea into action via a project that provides this functionality.

Vision System Enables Overlaid Images
In this project article, learn how these Cornell students Daniel Edens and Elise Weir designed a system to overlay images from a visible light camera and an infrared camera. They use software running on a PIC32 MCU to interface the two types of cameras. The MCU does the computation to create the overlaid images, and displays them on an LCD screen.

DATA ACQUISITION AND MEASUREMENT

Data Acquisition Alternatives
While the fundamentals of data acquisition remain the same, its interfacing technology keeps evolving and changing. USB and PCI Express brought data acquisition off the rack, and onto the lab bench top. Today solutions are emerging that leverage Mini PCIe, Thunderbolt and remote web interfacing. Circuit Cellar’s Editor-in-Chief, Jeff Child, dives into the latest technology trends and product developments in data acquisition.

High-Side Current Sensing
Jeff Bachiochi says he likes being able to measure things—for example, being able to measure load current so he can predict how long a battery will last. With that in mind, he recently found a high-side current sensing device, Microchip’s EMC1701. In his article, Jeff takes you through the details of the device and how to make use of it in a battery-based system.

Power Analysis Capture with an MCU
Low-cost microcontrollers integrate many powerful peripherals in them. You can even perform data capture directly to internal memory. In his article, Colin O’Flynn uses the ChipWhisperer-Nano as a case study in how you might use such features which would otherwise require external programmable logic.

TOOLS AND TECHNIQUES FOR EMBEDDED SYSTEM DESIGN

Easing into the IoT Cloud (Part 2)
In Part 1 of this article series Brian Millier examined some of the technologies and services available today enabling you to ease into the IoT cloud. Now, in Part 2, he discusses the hardware features of the Particle IoT modules, as well as the circuitry and program code for the project. He also explores the integration of a Raspberry Pi solution with the Particle cloud infrastructure.

Hierarchical Menus for Touchscreens
In his December article, Aubrey Kagan discussed his efforts to build a display subsystem and GUI for embedded use based on a Noritake touchscreen display. This time he shares how he created a menu system within the constraints of the Noritake graphical display system. He explains how he made good use of Microsoft Excel worksheets as a tool for developing the menu system.

Real Schematics (Part 2)
The first part of this article series on the world of real schematics ended last month with wiring. At high frequencies PCBs suffer from the same parasitic effects as any other type of wiring. You can describe a transmission line as consisting of an infinite number of infinitesimal resistors, inductors and capacitors spread along its entire length. In this article George Novacek looks at real schematics from a transmission line perspective.

Chip-Level Solutions Feed AI Needs

Embedded Supercomputing

Gone are the days when supercomputing meant big, rack-based systems in an air conditioned room. Today, embedded processors, FPGAs and GPUs are able to do AI and machine learning operations, enabling new types of local decision making in embedded systems.

By Jeff Child, Editor-in-Chief

Embedded computing technology has evolved way past the point now where complete system functionality on a single chip is remarkable. Today, the levels of compute performance and parallel processing on an IC means that what were once supercomputing levels of capabilities can now be implemented in in chip-level solutions.

While supercomputing has become a generalized term, what system developers are really interested in are crafting artificial intelligence, machine learning and neural networking using today’s embedded processing. Supplying the technology for these efforts are the makers of leading-edge embedded processors, FPGAs and GPUs. In these tasks, GPUs are being used for “general-purpose computing on GPUs”, a technique also known as GPGPU computing.

With all that in mind, embedded processor, GPU and FPGA companies have rolled out a variety of solutions over the last 12 months, aimed at performing AI, machine learning and other advanced computing functions for several demanding embedded system application segments.

FPGAS Take AI Focus

Back March, FPGA vendor Xilinx announced its plans to launch a new FPGA product category it calls its adaptive compute acceleration platform (ACAP). Following up on that, in October the company unveiled Versal—the first of its ACAP implementations. Versal ACAPs combine scalar processing engines, adaptable hardware engines and intelligent engines with advanced memory and interfacing technologies to provide heterogeneous acceleration for any application. But even more importantly, according to Xilinx, the Versal ACAP’s hardware and software can be programmed and optimized by software developers, data scientists and hardware developers alike. This is enabled by a host of tools, software, libraries, IP, middleware and frameworks that facilitate industry-standard design flows.

Built on TSMC’s 7-nm FinFET process technology, the Versal portfolio combines software programmability with domain-specific hardware acceleration and adaptability. The portfolio includes six series of devices architected to deliver scalability and AI inference capabilities for a host of applications across different markets, from cloud to networking to wireless communications to edge computing and endpoints.

The portfolio includes the Versal Prime series, Premium series and HBM series, which are designed to deliver high performance, connectivity, bandwidth, and integration for the most demanding applications. It also includes the AI Core series, AI Edge series and AI RF series, which feature the AI Engine (Figure 1). The AI Engine is a new hardware block designed to address the emerging need for low-latency AI inference for a wide variety of applications and also supports advanced DSP implementations for applications like wireless and radar.

Figure 1
Xilinx’s AI Engine is a new hardware block designed to address the emerging need for low-latency AI inference for a wide variety of applications. It also supports advanced DSP implementations for applications like wireless and radar.

It is tightly coupled with the Versal Adaptable Hardware Engines to enable whole application acceleration, meaning that both the hardware and software can be tuned to ensure maximum performance and efficiency. The portfolio debuts with the Versal Prime series, delivering broad applicability across multiple markets and the Versal AI Core series, delivering an estimated 8x AI inference performance boost compared to industry-leading GPUs, according to Xilinx.

Low-Power AI Solution

Following the AI trend, back in May Lattice Semiconductor unveiled Lattice sensAI, a technology stack that combines modular hardware kits, neural network IP cores, software tools, reference designs and custom design services. In September the company unveiled expanded features of the sensAI stack designed for developers of flexible machine learning inferencing in consumer and industrial IoT applications. Building on the ultra-low power (1 mW to 1 W) focus of the sensAI stack, Lattice released new IP cores, reference designs, demos and hardware development kits that provide scalable performance and power for always-on, on-device AI applications.

Embedded system developers can build a variety of solutions enabled by sensAI. They can build stand-alone iCE40 UltraPlus/ECP5 FPGA based always-on, integrated solutions, with latency, security and form factor benefits. Alternatively, they can use CE40 UltraPlus as an always-on processor that detects key phrases or objects, and wakes-up a high-performance AP SoC / ASIC for further analytics only when required, reducing overall system power consumption. And, finally, you can use the scalable performance/power benefits of ECP5 for neural network acceleration, along with I/O flexibility to seamlessly interface to on-board legacy devices including sensors and low-end MCUs for system control.

Figure 2
Human face detection application example. iCE40 UlraPlus enables AI with an always-on image sensor, while consuming less than 1 mW of active power.

Updates to the sensAI stack include a new CNN (convolutional neural networks) Compact Accelerator IP core for improved accuracy on iCE40 UltraPlus FPGA and enhanced CNN Accelerator IP core for improved performance on ECP5 FPGAs. Software tools include an updated neural network compiler tool with improved ease-of-use and both Caffe and TensorFlow support for iCE40 UltraPlus FPGAs. Also provided are reference designs enabling human presence detection and hand gesture recognition reference designs and demos (Figure 2). New iCE40 UltraPlus development platform support includes a Himax HM01B0 UPduino shield and DPControl iCEVision board.. …

Read the full article in the December 341 issue of Circuit Cellar

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December Circuit Cellar: Sneak Preview

The December issue of Circuit Cellar magazine is coming soon. Don’t miss this last issue of Circuit Cellar in 2018. Pages and pages of great, in-depth embedded electronics articles prepared for you to enjoy.

Not a Circuit Cellar subscriber?  Don’t be left out! Sign up today:

 

Here’s a sneak preview of December 2018 Circuit Cellar:

AI, FPGAs and EMBEDDED SUPERCOMPUTING

Embedded Supercomputing
Gone are the days when supercomputing levels of processing required a huge, rack-based systems in an air-conditioned room. Today, embedded processors, FPGAs and GPUs are able to do AI and machine learning kinds of operation, enable new types of local decision making in embedded systems. In this article, Circuit Cellar’s Editor-in-Chief, Jeff Child, looks at these technology and trends driving embedded supercomputing.

Convolutional Neural Networks in FPGAs
Deep learning using convolutional neural networks (CNNs) can offer a robust solution across a wide range of applications and market segments. In this article written for Microsemi, Ted Marena illustrates that, while GPUs can be used to implement CNNs, a better approach, especially in edge applications, is to use FPGAs that are aligned with the application’s specific accuracy and performance requirements as well as the available size, cost and power budget.

NOT-TO-BE-OVERLOOKED ENGINEERING ISSUES AND CHOICES

DC-DC Converters
DC-DC conversion products must juggle a lot of masters to push the limits in power density, voltage range and advanced filtering. Issues like the need to accommodate multi-voltage electronics, operate at wide temperature ranges and serve distributed system requirements all add up to some daunting design challenges. This Product Focus section updates readers on these technology trends and provides a product gallery of representative DC-DC converters.

Real Schematics (Part 1)
Our magazine readers know that each issue of Circuit Cellar has several circuit schematics replete with lots of resistors, capacitors, inductors and wiring. But those passive components don’t behave as expected under all circumstances. In this article, George Novacek takes a deep look at the way these components behave with respect to their operating frequency.

Do you speak JTAG?
While most engineers have heard of JTAG or have even used JTAG, there’s some interesting background and capabilities that are so well know. Robert Lacoste examines the history of JTAG and looks at clever ways to use it, for example, using a cheap JTAG probe to toggle pins on your design, or to read the status of a given I/O without writing a single line of code.

PUTTING THE INTERNET-OF-THINGS TO WORK

Industrial IoT Systems
The Industrial Internet-of-Things (IIoT) is a segment of IoT technology where more severe conditions change the game. Rugged gateways and IIoT edge modules comprise these systems where the extreme temperatures and high vibrations of the factory floor make for a demanding environment. Here, Circuit Cellar’s Editor-in-Chief, Jeff Child, looks at key technology and product drives in the IIoT space.

Internet of Things Security (Part 6)
Continuing on with his article series on IoT security, this time Bob Japenga returns to his efforts to craft a checklist to help us create more secure IoT devices. This time he looks at developing a checklist to evaluate the threats to an IoT device.

Applying WebRTC to the IoT
Web Real-time Communications (WebRTC) is an open-source project created by Google that facilitates peer-to-peer communication directly in the web browser and through mobile applications using application programming interfaces. In her article, Callstats.io’s Allie Mellen shows how IoT device communication can be made easy by using WebRTC. With WebRTC, developers can easily enable devices to communicate securely and reliably through video, audio or data transfer.

WI-FI AND BLUETOOTH IN ACTION

IoT Door Security System Uses Wi-Fi
Learn how three Cornell students, Norman Chen, Ram Vellanki and Giacomo Di Liberto, built an Internet connected door security system that grants the user wireless monitoring and control over the system through a web and mobile application. The article discusses the interfacing of a Microchip PIC32 MCU with the Internet and the application of IoT to a door security system.

Self-Navigating Robots Use BLE
Navigating indoors is a difficult but interesting problem. Learn how these two Cornell students, Jane Du and Jacob Glueck, used Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE) 4.0 chips to enable wheeled, mobile robots to navigate towards a stationary base station. The robot detects its proximity to the station based on the strength of the signal and moves towards what it believes to be the signal source.

IN-DEPTH PROJECT ARTICLES WITH ALL THE DETAILS

Sun Tracking Project
Most solar panel arrays are either fixed-position, or have a limited field of movement. In this project article, Jeff Bachiochi set out to tackle the challenge of a sun tracking system that can move your solar array to wherever the sun is coming from. Jeff’s project is a closed-loop system using severs, opto encoders and the Microchip PIC18 microcontroller.

Designing a Display System for Embedded Use
In this project article, Aubrey Kagan takes us through the process of developing an embedded system user interface subsystem—including everything from display selection to GUI development to MCU control. For the project he chose a 7” Noritake GT800 LCD color display and a Cypress Semiconductor PSoC5LP MCU.

COM Express Card Sports 3 GHz Core i3 Processor

Congatec has introduced a Computer-on-Module for the entry-level of high-end embedded computing based on Intel’s latest Core i3-8100H processor platform. The board’s fast 16 PCIe Gen 3.0 lanes make it suited for all new artificial intelligence (AI) and machine learning applications requiring multiple GPUs for massive parallel processing.

The new conga-TS370 COM Express Basic Type 6 Computer-on-Module with quad-core Intel Core i3 8100H processor offers a 45 W TDP configurable to 35 W, supports 6 MB cache and provides up to 32 GB dual-channel DDR4 2400 memory. Compared to the preceding 7th generation of Intel Core processors, the improved memory bandwidth also helps to increase the graphics and GPGPU performance of the integrated new Intel UHD630 graphics, which additionally features an increased maximum dynamic frequency of up to 1.0 GHz for its 24 execution units. It supports up to three independent 4K displays with up to 60 Hz via DP 1.4, HDMI, eDP and LVDS.

Embedded system designers can now switch from eDP to LVDS purely by modifying the software without any hardware changes. The module further provides exceptionally high bandwidth I/Os including 4x USB 3.1 Gen 2 (10 Gbit/s), 8x USB 2.0 and 1x PEG and 8 PCIe Gen 3.0 lanes for powerful system extensions including Intel Optane memory. All common Linux operating systems as well as the 64-bit versions of Microsoft Windows 10 and Windows 10 IoT are supported. Congatec’s personal integration support rounds off the feature set. Additionally, Congatec also offers an extensive range of accessories and comprehensive technical services, which simplify the integration of new modules into customer-specific solutions.

Congatec | www.congatec.com

MPU Targets AI-Based Imaging Processing

Renesas Electronics has now developed a new RZ/A2M microprocessor (MPU) to expand the use of artificial intelligence (e-AI) solutions to high-end applications. The new MPU delivers 10 times the image processing performance of its predecessor, the RZ/A1, and incorporates Renesas’ exclusive Dynamically Reconfigurable Processor (DRP), which achieves real-time image processing at low power consumption. This allows applications incorporating embedded devices–such as smart appliances, service robots, and compact industrial machinery–to carry out image recognition employing cameras and other AI functions while maintaining low power consumption, and accelerating the realization of intelligent endpoints.
Currently, there are several challenges to using AI in the operational technology (OT) field, such as difficulty transferring large amounts of sensor data to the cloud for processing, and delays waiting for AI judgments to be transferred back from the cloud. Renesas already offers AI unit solutions that can detect previously invisible faults in real time by minutely analyzing oscillation waveforms from motors or machines. To accelerate the adoption of AI in the OT field, Renesas has developed the RZ/A2M with DRP, which makes possible image-based AI functionality requiring larger volumes of data and more powerful processing performance than achievable with waveform measurement and analysis.

Since real-time image processing can be accomplished while consuming very little power, battery-powered devices can perform tasks such as real-time image recognition based on camera input, biometric authentication using fingerprints or iris scans, and high-speed scanning by handheld scanners. This solves several issues associated with cloud-based approaches, such as the difficulty of achieving real-time performance, assuring privacy and maintaining security.

The RZ/A2M with DRP is a new addition to the RZ/A Series lineup of MPUs equipped with large capacity on-chip RAM, which eliminates the need for external DRAM. The RZ/A Series MPUs address applications employing human-machine interface (HMI) functionality, and the RZ/A2M adds to this capability with features ideal for applications using cameras. It supports the MIPI camera interface, widely used in mobile devices, and is equipped with a DRP for high-speed image processing.

Renesas has also boosted network functionality with the addition of two-channel Ethernet support, and enhanced secure functionality with an on-chip hardware encryption accelerator. These features enable safe and secure network connectivity, making the new RZ/A2M best suited for a wide range of systems employing image recognition, from home appliances to industrial machinery.

Samples of the RZ/A2M with DRP are available now. The RZ/A2M MPUs are offered with a development board, reference software, and DRP image-processing library, allowing customers to begin evaluating HMI function and image processing performance. Mass production is scheduled to start in the first quarter of 2019, and monthly production volume for all RZ/A2M versions is anticipated to reach a combined 400,000 units by 2021.

Renesas Electronics | www.renesas.com

SoC Provides Neural Network Acceleration

Brainchip has claimed itself as the first company to bring a production spiking neural network architecture to market. Called the Akida Neuromorphic System-on-Chip (NSoC), the device is small, low cost and low power, making it well-suited for edge applications such as advanced driver assistance systems (ADAS), autonomous vehicles, drones, vision-guided robotics, surveillance and machine vision systems. Its scalability allows users to network many Akida devices together to perform complex neural network training and inferencing for many markets including agricultural technology (AgTech), cybersecurity and financial technology (FinTech).
According to Lou DiNardo, BrainChip CEO, Akida, which is Greek for ‘spike,’ represents the first in a new breed of hardware solutions for AI. Artificial intelligence at the edge is going to be as significant and prolific as the microcontroller.

The Akida NSoC uses a pure CMOS logic process, ensuring high yields and low cost. Spiking neural networks (SNNs) are inherently lower power than traditional convolutional neural networks (CNNs), as they replace the math-intensive convolutions and back-propagation training methods with biologically inspired neuron functions and feed-forward training methodologies. BrainChip’s research has determined the optimal neuron model and training methods, bringing unprecedented efficiency and accuracy. Each Akida NSoC has effectively 1.2 million neurons and 10 billion synapses, representing 100 times better efficiency than neuromorphic test chips from Intel and IBM. Comparisons to leading CNN accelerator devices show similar performance gains of an order of magnitude better images/second/watt running industry standard benchmarks such as CIFAR-10 with comparable accuracy.

The Akida NSoC is designed for use as a stand-alone embedded accelerator or as a co-processor. It includes sensor interfaces for traditional pixel-based imaging, dynamic vision sensors (DVS), Lidar, audio, and analog signals. It also has high-speed data interfaces such as PCI-Express, USB, and Ethernet. Embedded in the NSoC are data-to-spike converters designed to optimally convert popular data formats into spikes to train and be processed by the Akida Neuron Fabric.

Spiking neural networks are inherently feed-forward dataflows, for both training and inference. Ingrained within the Akida neuron model are innovative training methodologies for supervised and unsupervised training. In the supervised mode, the initial layers of the network train themselves autonomously, while in the final fully-connected layers, labels can be applied, enabling these networks to function as classification networks. The Akida NSoC is designed to allow off-chip training in the Akida Development Environment, or on-chip training. An on-chip CPU is used to control the configuration of the Akida Neuron Fabric as well as off-chip communication of metadata.

The Akida Development Environment is available now for early access customers to begin the creation, training, and testing of spiking neural networks targeting the Akida NSoC. The Akida NSoC is expected to begin sampling in Q3 2019.

Brainchip | www.brainchip.com

Multiphase PMICs Boast High Efficiency and Small Footprint

Renesas Electronics has announced three programmable power management ICs (PMICs) that offer high power efficiency and small footprint for application processors in smartphones and tablets: the ISL91302B, ISL91301A, and ISL91301B PMICs. The PMICs also deliver power to artificial intelligence (AI) processors, FPGAs and industrial microprocessors (MPUs). They are also well-suited for powering the supply rails in solid-state drives (SSDs), optical transceivers, and a wide range of consumer, industrial and networking devices. The ISL91302B dual/single output, multiphase PMIC provides up to 20 A of output current and 94 percent peak efficiency in a 70 mm2 solution size that is more than 40% smaller than competitive PMICs.
In addition to the ISL91302B, Renesas’ ISL91301A triple output PMIC and ISL91301B quad output PMIC both deliver up to 16 A of output power with 94% peak efficiency. The new programmable PMICs leverage Renesas’ R5 Modulation Technology to provide fast single-cycle transient response, digitally tuned compensation, and ultra-high 6 MHz (max) switching frequency during load transients. These features make it easier for power supply designers to design boards with 2 mm x 2 mm, 1mm low profile inductors, small capacitors and only a few passive components.

Renesas PMICs also do not require external compensation components or external dividers to set operating conditions. Each PMIC dynamically changes the number of active phases for optimum efficiency at all output currents. Their low quiescent current, superior light load efficiency, regulation accuracy, and fast dynamic response significantly extend battery life for today’s feature-rich, power hungry devices.

Key Features of ISL91302B PMIC:

  • Available in three factory configurable options for one or two output rails:
    • Dual-phase (2 + 2) configuration supporting 10 A from each output
    • Triple-phase (3 + 1) configuration supporting 15 A from one output and  5A from the second output
    • Quad-phase (4 + 0) configuration supporting 20A from one output
  • Small solution size: 7 mm x 10 mm for 4-phase design
  • Input supply voltage range of 2.5 V to 5.5 V
  • I2C or SPI programmable Vout from 0.3 V to 2 V
  • R5 modulator architecture balances current loads with smooth phase adding and dropping for power efficiency optimization
  • Provides 75 μA quiescent current in discontinuous current mode (DCM)
  • Independent dynamic voltage scaling for each output
  • ±0.7percent system accuracy for -10°C to 85°C with remote voltage sensing
  • Integrated telemetry ADC senses phase currents, output current, input/output voltages, and die temperature, enabling PMIC diagnostics during operation
  • Soft-start and fault protection against under voltage (UV), over voltage (OV), over current (OC), over temperature (OT), and short circuit

Key Features of ISL91301A and ISL91301B PMICs

  • Available in two factory configurable options:
    • ISL91301A: dual-phase, three output rails configured as 2+1+1 phase
    • ISL91301B: single-phase, four output rails configured as 1+1+1+1 phase
  • 4A per phase for 2.8 V to 5.5 V supply voltage
  • 3A per phase for 2.5 V to 5.5 V supply voltage
  • Small solution size: 7 mm x 10 mm for 4-phase design
  • I2C or SPI programmable Vout from 0.3 V to 2 V
  • Provides 62μA quiescent current in DCM mode
  • Independent dynamic voltage scaling for each output
  • ±0.7percent system accuracy for -10°C to 85°C with remote voltage sensing
  • Soft-start and fault protection against UV, OV, OC, OT, and short circuit

Pricing and Availability

The ISL91302B dual/single output PMIC is available now in a 2.551 mm x 3.670 ball WLCSP package and is priced at $3.90 in 1k quantities. For more information on the ISL91302B, please visit: www.intersil.com/products/isl91302B.

The ISL91301A triple-output PMIC and ISL91301B quad-output PMIC are available now in 2.551 mm x 2.87 mm, 42-ball WLCSP packages, both priced at $3.12 in 1k quantities. For more information on the ISL91301A, please visit: www.intersil.com/products/isl91301A. For more information on the ISL91301B, please visit: www.intersil.com/products/isl91301B.

Renesas Electronics | www.renesas.com