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.

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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.

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