SUPPLEMENT SMART SOLUTIONS ANALOGUE ACCELEROMETERS FOR INDUSTRIAL MARKET
supported bandwidths range from 4.5kHz to 8kHz. Both sensors have built-in self-test functions, consume only 0.25mA at 3.3V and are delivered in 10-pin 3mm x 3mm LGA packages. "The fast pace of digital sensor
A
new family of medium-g, tri- axis analogue accelerometers
has been released by Kionix, a Rohm group company. Initially two versions are available: the KX220- 1071 and the KX220-1072, providing +/-20g and +/-40g ranges respectively. Maximum
technologies in the consumer space is well known," states Nader Sadrzadeh, CEO of Kionix. "Industrial companies are now recognising the benefits of these technologies and are accelerating their investment in them to bring greater efficiency, lower costs, and improved operations. But unlike the consumer market, with its short upgrade cycles, it often doesn't make sense in the industrial sector to cast aside existing infrastructure
and directly upgrade to digital solutions due to the significant capital already invested, the long lifetimes of equipment, and the need to ensure continuity. So although digital rules in the consumer world, analogue is still very alive and often preferred in industrial applications." Since the typical applications of
industrial sensors are monitoring machines and physical processes, the g-range and bandwidth requirements are typically higher than those in hand held consumer devices. These sensors are targeted to assist in applications ranging from vibration monitoring to shock and impact detection and measurement.
www.rohm.com/eu
A new series of bulkhead hoods with increased resistance to external influences including dust, dirt and liquids has been designed by Harting. The special flange of the Han M die-cast
aluminium housing prevents water from penetrating into the connector – thus protecting the contact points inside the connector. The circumferential collar not only seals the
housing externally, but also prevents the seal from slipping inwards or outwards.
www.harting.com
PROTECTION AGAINST WATER DAMAGE
INTEL’S NEW SELF-LEARNING CHIP PROMISES TO ACCELERATE ARTIFICIAL INTELLIGENCE
Imagine a future where complex decisions could be made faster and adapt over time. Where societal and industrial problems can be autonomously solved using learned experiences. It’s a future where first
responders using image-recognition applications can analyse streetlight
camera images and quickly solve missing or abducted person reports. It’s a future where stoplights automatically adjust their timing to sync with the flow of traffic, reducing gridlock and optimising starts and stops. It’s a future where robots are more autonomous and performance efficiency is dramatically increased. An increasing need for collection, analysis and decision-making from
highly dynamic and unstructured natural data is driving demand for computing that may outpace both classic CPU and GPU architectures. To keep pace with the evolution of technology and to drive computing beyond PCs and servers, Intel has been working for the past six years on specialised architectures that can accelerate classic compute platforms. Intel has also recently advanced investments and R&D in artificial intelligence (AI) and neuromorphic computing. As part of an effort within Intel Labs, Intel has developed a first-of-its-
kind self-learning neuromorphic chip called Loihi – that mimics how the brain functions by learning to operate based on various modes of feedback from the environment. This extremely energy-efficient chip, which uses the data to learn and make inferences, gets smarter over time and does not need to be trained in the traditional way. It takes a novel approach to computing asynchronous spiking. Neuromorphic computing draws inspiration from our current
understanding of the brain’s architecture and its associated computations. The brain’s neural networks relay information with pulses or spikes, modulate the synaptic strengths or weight of the interconnections based on timing of these spikes, and store these changes locally at the interconnections. Intelligent behaviours emerge from the cooperative and competitive interactions between multiple regions within the brain’s neural networks and its environment. Machine learning models such as deep learning have made tremendous
recent advancements by using extensive training datasets to recognise objects and events. However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these machine learning systems do not generalise well.
S4 OCTOBER 2017 | ELECTRONICS
The potential benefits from self-learning chips are limitless. One example provides a person’s heartbeat reading under various conditions – after jogging, following a meal or before going to bed – to a neuromorphic-based system that parses the data to determine a “normal” heartbeat. The system can then continuously monitor incoming heart data in order to flag patterns that do not match the “normal” pattern. The system could then be personalised. The Loihi research test chip includes digital circuits that mimic the brain’s
basic mechanics, making machine learning faster and more efficient while requiring lower compute power. Neuromorphic chip models draw inspiration from how neurons communicate and learn, using spikes and plastic synapses that can be modulated based on timing. This could help computers self- organise and make decisions based on patterns and associations. The Loihi test chip offers highly flexible on-chip learning and combines
training and inference on a single chip. This allows machines to be autonomous and to adapt in real time instead of waiting for the next update from the cloud. Researchers have demonstrated learning at a rate that is a 1 million times improvement compared with other typical spiking neural nets as measured by total operations to achieve a given accuracy when solving MNIST digit recognition problems. Compared to technologies such as convolutional neural networks and deep learning neural networks, the Loihi test chip uses many fewer resources on the same task. The self-learning capabilities prototyped by this test chip have enormous
potential to improve automotive and industrial applications as well as personal robotics – any application that would benefit from autonomous operation and continuous learning in an unstructured environment. For example, recognising the movement of a car or bike. Spurred by advances in computing and algorithmic innovation, the transformative power of AI is expected to impact society on a spectacular scale. Both general purpose compute and custom hardware and software come
into play at all scales. The Intel Xeon Phi processor, widely used in scientific computing, has generated some of the world’s biggest models to interpret large-scale scientific problems, and the Movidius Neural Compute Stick is an example of a 1-watt deployment of previously trained models. As AI workloads grow more diverse and complex, they will test the limits
of today’s dominant compute architectures and precipitate new disruptive approaches. Looking to the future, Intel believes that neuromorphic computing offers a way to provide exascale performance in a construct inspired by how the brain works. Intel’s vision for developing innovative compute architectures remains steadfast, and advises it knows what the future of computing looks like because the company are building it today.
www.intel.com
/ ELECTRONICS
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