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Intro to reed switches, magnets, magnetic fields

This brief introductory video on the DigiKey site offers tips for engineers designing with reed switches. Dr. Stephen Day, Ph.D. from Coto Technology gives a solid overview on reed switches -- complete with real-world application examples -- and a detailed explanation of how they react to magnetic fields.
View the video.

Bi-color LEDs to light up your designs

Created with engineers and OEMs in mind, SpectraBright Series SMD RGB and Bi-Color LEDs from Visual Communi-cations Company (VCC) deliver efficiency, design flexibility, and control for devices in a range of industries, including mil-aero, automated guided vehicles, EV charging stations, industrial, telecom, IoT/smart home, and medical. These 50,000-hr bi-color and RGB options save money and space on the HMI, communicating two or three operating modes in a single component.
Learn more.

All about slip rings: How they work and their uses

Rotary Systems has put together a really nice basic primer on slip rings -- electrical collectors that carry a current from a stationary wire into a rotating device. Common uses are for power, proximity switches, strain gauges, video, and Ethernet signal transmission. This introduction also covers how to specify, assembly types, and interface requirements. Rotary Systems also manufactures rotary unions for fluid applications.
Read the overview.

Seifert thermoelectric coolers from AutomationDirect

Automation-Direct has added new high-quality and efficient stainless steel Seifert 340 BTU/H thermoelectric coolers with 120-V and 230-V power options. Thermoelectric coolers from Seifert use the Peltier Effect to create a temperature difference between the internal and ambient heat sinks, making internal air cooler while dissipating heat into the external environment. Fans assist the convective heat transfer from the heat sinks, which are optimized for maximum flow.
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EMI shielding honeycomb air vent panel design

Learn from the engineering experts at Parker how honeycomb air vent panels are used to help cool electronics with airflow while maintaining electromagnetic interference (EMI) shielding. Topics include: design features, cell size and thickness, platings and coatings, and a stacked design called OMNI CELL construction. These vents can be incorporated into enclosures where EMI radiation and susceptibility is a concern or where heat dissipation is necessary. Lots of good info.
Read the Parker blog.

What is 3D-MID? Molded parts with integrated electronics from HARTING

3D-MID (three-dimensional mechatronic integrated devices) technology combines electronic and mechanical functionalities into a single, 3D component. It replaces the traditional printed circuit board and opens up many new opportunities. It takes injection-molded parts and uses laser-direct structuring to etch areas of conductor structures, which are filled with a copper plating process to create very precise electronic circuits. HARTING, the technology's developer, says it's "Like a PCB, but 3D." Tons of possibilities.
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Loss-free conversion of 3D/CAD data

CT CoreTech-nologie has further developed its state-of-the-art CAD converter 3D_Evolution and is now introducing native interfaces for reading Solidedge and writing Nx and Solidworks files. It supports a wide range of formats such as Catia, Nx, Creo, Solidworks, Solidedge, Inventor, Step, and Jt, facilitating smooth interoperability between different systems and collaboration for engineers and designers in development environments with different CAD systems.
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Top 5 reasons for solder joint failure

Solder joint reliability is often a pain point in the design of an electronic system. According to Tyler Ferris at ANSYS, a wide variety of factors affect joint reliability, and any one of them can drastically reduce joint lifetime. Properly identifying and mitigating potential causes during the design and manufacturing process can prevent costly and difficult-to-solve problems later in a product lifecycle.
Read this informative ANSYS blog.

Advanced overtemp detection for EV battery packs

Littelfuse has introduced TTape, a ground-breaking over-temperature detection platform designed to transform the management of Li-ion battery systems. TTape helps vehicle systems monitor and manage premature cell aging effectively while reducing the risks associated with thermal runaway incidents. This solution is ideally suited for a wide range of applications, including automotive EV/HEVs, commercial vehicles, and energy storage systems.
Learn more.

Benchtop ionizer for hands-free static elimination

EXAIR's Varistat Benchtop Ionizer is the latest solution for neutralizing static on charged surfaces in industrial settings. Using ionizing technology, the Varistat provides a hands-free solution that requires no compressed air. Easily mounted on benchtops or machines, it is manually adjustable and perfect for processes needing comprehensive coverage such as part assembly, web cleaning, printing, and more.
Learn more.

LED light bars from AutomationDirect

Automation-Direct adds CCEA TRACK-ALPHA-PRO series LED light bars to expand their offering of industrial LED fixtures. Their rugged industrial-grade anodized aluminum construction makes TRACKALPHA-PRO ideal for use with medium to large-size industrial machine tools and for use in wet environments. These 120 VAC-rated, high-power LED lights provide intense, uniform lighting, with up to a 4,600-lumen output (100 lumens per watt). They come with a standard bracket mount that allows for angle adjustments. Optional TACLIP mounts (sold separately) provide for extra sturdy, vibration-resistant installations.
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World's first metalens fisheye camera

2Pi Optics has begun commercial-ization of the first fisheye camera based on the company's proprietary metalens technology -- a breakthrough for electronics design engineers and product managers striving to miniaturize the tiny digital cameras used in advanced driver-assistance systems (ADAS), AR/VR, UAVs, robotics, and other industrial applications. This camera can operate at different wavelengths -- from visible, to near IR, to longer IR -- and is claimed to "outperform conventional refractive, wide-FOV optics in all areas: size, weight, performance, and cost."
Learn more.

Orbex offers two fiber optic rotary joint solutions

Orbex Group announces its 700 Series of fiber optic rotary joint (FORJ) assemblies, supporting either single or multi-mode operation ideal for high-speed digital transmission over long distances. Wavelengths available are 1,310 or 1,550 nm. Applications include marine cable reels, wind turbines, robotics, and high-def video transmission. Both options feature an outer diameter of 7 mm for installation in tight spaces. Construction includes a stainless steel housing.
Learn more.

Mini tunnel magneto-resistance effect sensors

Littelfuse has released its highly anticipated 54100 and 54140 mini Tunnel Magneto-Resistance (TMR) effect sensors, offering unmatched sensitivity and power efficiency. The key differentiator is their remarkable sensitivity and 100x improvement in power efficiency compared to Hall Effect sensors. They are well suited for applications in position and limit sensing, RPM measurement, brushless DC motor commutation, and more in various markets including appliances, home and building automation, and the industrial sectors.
Learn more.

Panasonic solar and EV components available from Newark

Newark has added Panasonic Industry's solar inverters and EV charging system components to their power portfolio. These best-in-class products help designers meet the growing global demand for sustainable and renewable energy mobility systems. Offerings include film capacitors, power inductors, anti-surge thick film chip resistors, graphite thermal interface materials, power relays, capacitors, and wireless modules.
Learn more.

High speed and low power: New chip could bring neural networks to handhelds and appliances

MIT researchers have developed a special-purpose chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power consumption 94 to 95 percent. That could make it practical to run neural networks locally on smartphones or even to embed them in household appliances. [Image: Chelsea Turner/MIT]

 

 

 

 

By Larry Hardesty, MIT

Most recent advances in artificial-intelligence systems such as speech- or face-recognition programs have come courtesy of neural networks, densely interconnected meshes of simple information processors that learn to perform tasks by analyzing huge sets of training data.

But neural nets are large, and their computations are energy intensive, so they're not very practical for handheld devices. Most smartphone apps that rely on neural nets simply upload data to internet servers, which process it and send the results back to the phone.

Now, MIT researchers have developed a special-purpose chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power consumption 94 to 95 percent. That could make it practical to run neural networks locally on smartphones or even to embed them in household appliances.

"The general processor model is that there is a memory in some part of the chip, and there is a processor in another part of the chip, and you move the data back and forth between them when you do these computations," says Avishek Biswas, an MIT graduate student in electrical engineering and computer science, who led the new chip's development.

"Since these machine-learning algorithms need so many computations, this transferring back and forth of data is the dominant portion of the energy consumption. But the computation these algorithms do can be simplified to one specific operation, called the dot product. Our approach was, can we implement this dot-product functionality inside the memory so that you don't need to transfer this data back and forth?"

Biswas and his thesis advisor, Anantha Chandrakasan, dean of MIT's School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, describe the new chip in a paper that Biswas presented last week at the International Solid State Circuits Conference.

Back to analog
Neural networks are typically arranged into layers. A single processing node in one layer of the network will generally receive data from several nodes in the layer below and pass data to several nodes in the layer above. Each connection between nodes has its own "weight," which indicates how large a role the output of one node will play in the computation performed by the next. Training the network is a matter of setting those weights.

A node receiving data from multiple nodes in the layer below will multiply each input by the weight of the corresponding connection and sum the results. That operation -- the summation of multiplications -- is the definition of a dot product. If the dot product exceeds some threshold value, the node will transmit it to nodes in the next layer, over connections with their own weights.

A neural net is an abstraction: The "nodes" are just weights stored in a computer's memory. Calculating a dot product usually involves fetching a weight from memory, fetching the associated data item, multiplying the two, storing the result somewhere, and then repeating the operation for every input to a node. Given that a neural net will have thousands or even millions of nodes, that's a lot of data to move around.

But that sequence of operations is just a digital approximation of what happens in the brain, where signals traveling along multiple neurons meet at a "synapse," or a gap between bundles of neurons. The neurons' firing rates and the electrochemical signals that cross the synapse correspond to the data values and weights. The MIT researchers' new chip improves efficiency by replicating the brain more faithfully.

In the chip, a node's input values are converted into electrical voltages and then multiplied by the appropriate weights. Summing the products is simply a matter of combining the voltages. Only the combined voltages are converted back into a digital representation and stored for further processing.

The chip can thus calculate dot products for multiple nodes -- 16 at a time, in the prototype -- in a single step, instead of shuttling between a processor and memory for every computation.

All or nothing
One of the keys to the system is that all the weights are either 1 or -1. That means that they can be implemented within the memory itself as simple switches that either close a circuit or leave it open. Recent theoretical work suggests that neural nets trained with only two weights should lose little accuracy -- somewhere between 1 and 2 percent.

Biswas and Chandrakasan's research bears that prediction out. In experiments, they ran the full implementation of a neural network on a conventional computer and the binary-weight equivalent on their chip. Their chip's results were generally within 2 to 3 percent of the conventional network's.

"This is a promising real-world demonstration of SRAM-based in-memory analog computing for deep-learning applications," says Dario Gil, vice president of artificial intelligence at IBM. "The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays. It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future."

Published February 2018

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