One advantage that the IoT brought to design was the ability for a small local device to access the network’s virtually-unlimited computing power.  The Amazon Echo is a classic example: a low-cost local device that provided powerful speech recognition AI and an immense application library by way of its Internet connection. Now, some of that AI is moving into the local device to help minimize bandwidth and latency concerns by employing an efficient form of machine learning (ML) for smaller devices.

An example of what can be accomplished by placing AI in edge devices can be found in the article AI helps turn gas sensor into electronic nose. In this instance the ML that generates the sensor’s algorithms takes place during the design cycle, and the local device simply runs the algorithm. This is a first step in bring AI to the edge, but there are more to come.

To reach its full potential, AI at the edge will need to be self-adaptive. This means that the edge device will have to implement ML locally. How, exactly, this is to be done with the limited compute power edge devices typically have available is currently the subject of considerable research and development. Providing a form for information and idea exchange in local machine learning is the goal of the tinyML Foundation.

pable of learning their tasks without excessive developer effort. Source: TensorFlow

The foundation held its first industry event – the tinyML Summit – in 2019 and generated considerable interest along with participation by more than 90 companies. That event revealed three essential trends:

• Tiny ML-capable hardware is currently becoming “good enough” for many commercial applications with new and even better architectures on the horizon.
• Algorithms, networks, and models have seen significant size reduction, with many sized down to 100 kBytes and below.
• There is growing momentum demonstrated by technical progress and ecosystem development.

This result demonstrated that ML is not only coming to the edge, in some cases it is already there.

COVID-19 prevented a 2020 event, but for 2021 the tinyML Foundation created a free online event that recently concluded but should be available as an archive for registered attendees. In addition, the organization has developed a series of lectures called the tinyML Talks that are available on YouTube and other platforms.

The trend is clearly gaining traction. The organization’s sponsors now span the range from major hardware players such as Arm, Cypress Semiconductor, and Samsung to software start-ups focusing on low-power AI applications. Most are focused on either vision or audio (voice recognition) systems for now, but smart sensors are gaining ground as a viable application as well.

This trend bodes well for IoT developers. Creating compact, low-power devices with reasonable cost that perform complex tasks can be a developers nightmare using conventional programming techniques. Yet depending on connectivity to network-based AI processing for the device’s performance has its own drawbacks. Home networks are already becoming clogged with demands from streaming media and communications; adding a host of network-hogging smart devices can overload the typical home connection. The latency of network communications can also be an issue, as can be the total failure of device operation when the network is down.

Moving the AI to the edge – at least for basic functionality – solves most of these concerns. With ML in the edge device, developers can craft their systems to learn how to meet customer demands without the developers needing to exhaustively analyze use cases in advance. Having AI in the edge device reduces the need for network bandwidth, eliminates network latency issues, and ensures operation in the network’s absence. The efforts to expand tiny ML technology will help speed the movement of AI into IoT devices.

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Police in Scotland are getting ready to roll out a fleet of ‘cyber kiosks’ that will allow them to mine device data for evidence. The kiosks — PC-sized machines — have been designed to help investigations progress faster. At the moment, devices can be taken from witnesses, victims and suspects for months at a time, even if they contain no worthwhile evidence. According to Police Scotland, the kiosks will enable officers to quickly scan a device for evidence, and if relevant information is found, the device will be sent on for further investigation. If not, it can be returned to its owner straight away.

The kiosks are not able to save any data — they can only display it to an investigating officer. The only information the kiosks retain is details on how they have been used — by whom and at what times. The software is also able to segregate data based on type (such as messages or pictures) and date range, to help officers more quickly find what they’re looking for. Deputy chief constable Malcolm Graham said that “By quickly identifying devices which do and do not contain evidence, we can minimise the intrusion on people’s lives and provide a better service to the public.”

Police Scotland says it consulted a variety of groups and experts before commissioning the technology, and has given assurances that it will only examine a digital device where there is “a legal basis and where it is necessary, justified and proportionate to the incident or crime under investigation.” While the kiosks have the ability to bypass passwords and lockscreens, this will only be done after consultation with the police cybercrime unit. However, some critics have voiced concerns regarding data privacy and abuses of power — a growing narrative around the globe. The roll-out of the cyber kiosks – which will begin in Scotland on 20th January — comes only a few days after it emerged the FBI extracted data from a locked iPhone in the US, prompting fresh concerns over civil freedoms.

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Researchers of the Indian Institute of Technology Hyderabad have developed low power chips that can be used in Artificial Intelligence-powered devices. They have developed Magnetic quantum-dot cellular automata (MQCA) based on nanomagnetic logic architectural design methodology of approximate arithmetic circuits.

The researchers are working towards a vision of realizing resource-constrained Magnetic Chips for Ultra low power portable Artificial Intelligent applications.

A solution for difficult application

Many modern systems such as speech and face recognition systems and IoT enabled devices for remote health monitoring require highly computationally and energy-intensive neural networks. Hence, it is not practically affordable to perform these computations in the portable hand-held devices. With these major limitations, all the machine learning algorithms used in these Artificial Intelligent applications runs on remote systems.

These factors put forth a clear demand for low power chip design in the area of Artificial Intelligence. To address these issues, highly intensive convolutions should be performed using ultra low power, least energy-consuming, and area efficient devices, thus motivated us to explore the MQCA based nanomagnetic architecture designs for next-generation rebooting computing platform.

The IIT Hyderabad Researchers from the Advanced Embedded Systems and IC Design Laboratory, Department of Electrical Engineering, have conducted extensive research in this area and as a proof of concept demonstration, they have shown ‘Dipole coupled MQCA based efficient approximate nanomagnetic subtractor and adder design approach.’

Who did the research?

The research was undertaken by a team comprising Santhosh Sivasubramani, PhD Scholar, Advanced Embedded Systems and IC Design Laboratory, Department of Electrical Engineering, IIT Hyderabad, Amit Acharyya, Associate Professor, Department of Electrical Engineering, IIT Hyderabad, and Chandrajit Pal, Post-Doctoral Research Fellow, IIT Hyderabad.

The Research has been published in the journal by Nanotechnology (journal of Institute of Physics).

Why is this research unique?

Speaking about the outcomes and benefits of this Research, Amit Acharyya said, “We have computationally modelled, designed and implemented an arithmetic adder, subtractor and add/sub using nanomagnets which are the basic building blocks of performing AI computing.

We are aware that the emerging edge computing devices are handy in size as well as requiring low-power computation and are also tolerant to feeble decrease in precision.

The reported work of ours’ targets such devices, where there is a significant investment in the research towards making it low power without compromising on accuracy too much.

Performing AI computing on edge with approximate nanomagnetic logic deployed on the magnetic ICs is an attempt towards the futuristic computations. I hope this work paves the way towards achieving such a vision.”

Relevant to this age and time

To cater to the growing demand in processing and storing Big Data arising due to Digital India and other initiatives, the need for High-Performance Computing data centers is rapidly growing. This demand opens up the new avenues of computing paradigm.

One such emerging next-generation computing platform is MQCA based nanomagnetic computing which is energy efficient, consuming ultra-low-power and possessing reduced Static power dissipation and dynamic power dissipation.

Speaking about this research, Santhosh Sivasubramani said, “The proposed design methodology of performing approximate arithmetic computation using nanomagnets yields 50%-80% reduction in the number of nanomagnets and clock cycles without much degradation in the accuracy leading to area and energy efficiency in comparison to the traditional implementation of accurate nanomagnetic logic design. We achieved a 1-bit approximate full adder / subtractor implementation using only 4 individual nanomagnets which offers supremacy over existing designs contributing towards Rebooting Computing.

With these becoming successful, we now aim for a bigger goal by porting some power-hungry AI applications on such indigenously developed ultra-low-power computing platform.”

Intrinsic energy minimization nature and the non-volatility of nanomagnets aid MQCA based NML devices operate at ultra-low power in comparison to its CMOS counterpart enabling a power-hungry system design. Leveraging this inherent advantage of NML, it has been envisaged to possess significant potential in performing approximate computing with a tradeoff between accuracy and power consumption.

Such approximate computing architectures are deployed to perform computationally intensive tasks under the resource-constrained platform.

For example, emerging AI computing on edge devices for IoT applications, where a significant reduction in power consumption can be achieved with insignificant loss inaccuracy.

The power consumed by the modern chips are enormously high as the standby power required to maintain the logic states in the chip is equal to the power consumed by the chip during computation.

The science behind this project

Traditionally, electronic phenomena are used for information processing (CMOS Devices) and the magnetic phenomena are widely used for data storage (Hard Disks). However, the traditional CMOS devices consumes power supply (standby power) to maintain its ‘logic states’ required for computing information, thus making it volatile.

On the contrary, the emerging next-generation electronic devices using ‘dipole coupled nanomagnets’ for computing and information propagation requires no standby power to maintain its logic states thus making it non-volatile. Thus the magnetic chip design started emerging as potential alternative to CMOS based computing which faces challenges with the Moore’s law approaching towards its end.

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