The rise of artificial intelligence and impending end of Moore’s law means silicon chips are nearing the end of the line. Here are some alternatives.

SILICON has been making our computers work for almost half a century. Whether designed for graphics or number crunching, all information processing is done using a million-strong horde of tiny logic gates made from element number 14.

But silicon’s time may soon be up. Moore’s law – the prophecy which dictates that the number of silicon transistors on microprocessors doubles every two years – is grinding to a halt because there is a limit to how many can be squeezed on a chip.

The machine-learning boom is another problem. The amount of energy silicon-based computers use is set to soar as they crunch more of the massive data sets that algorithms in this field require. The Semiconductor Industry Association estimates that, on current trends, computing’s energy demands will outstrip the world’s total energy supply by 2040.

So research groups all over the world are building alternative systems that can handle large amounts of data without using silicon. All of them strive to be smaller and more power efficient than existing chips.

Unstable computing

Julie Grollier leads a group at the UMPhy lab near Paris that looks at how nanodevices can be engineered to work more like the human brain. Her team uses tiny magnetic particles for computation, specifically pattern recognition.

When magnetic particles are really small they become unstable and their magnetic fields start to oscillate wildly. By applying a current, the team has harnessed these oscillations to do basic computations. Scaled up, Grollier believes the technology could recognize patterns far faster than existing techniques.

It would also be less power-hungry. The magnetic auto-oscillators Grollier works with could use 100 times less power than their silicon counterparts. They can be 10,000 times smaller too.

Igor Carron, who launched Paris-based start-up LightOn in December, has another alternative to silicon chips: light.

Carron won’t say too much about how his planned LightOn computers will work, but they will have an optical system that processes bulky and unwieldy data sets so machine learning algorithms can deal with them more easily. It does this using a mathematical technique called random projection. This method has been known about since 1984, but has always involved too many computations for silicon chips to handle. Now, Carron and his colleagues are working on a way to do the whole operation with light.

“On current trends, computing’s energy demands could outstrip total supply by 2040“

What will these new ways of processing and learning from data make possible? Carron thinks machines that can learn without needing bulky processors will allow wearable computing to take off. They could also make the emerging “internet of things” – where computers are built into ordinary objects – far more powerful. These objects would no longer need to funnel data back and forth to data centres for processing. Instead, they will be able to do it on the spot.

Devices such as Grollier’s and Carron’s aren’t the only ones taking an alternative approach to computation. A group at Stanford University in California has built a chip containing 178 transistors out of carbon nanotubes, whose electrical properties make them more efficient switches than silicon transistors. And earlier this year, researchers at Ben-Gurion University in Israel and the Georgia Institute of Technology used DNA to build the world’s smallest diode, an electronic component used in computers.

For the time being, high-power silicon computers that handle massive amounts of data are still making huge gains in machine learning. But that exponential growth cannot continue forever. To really tap into and learn from all the world’s data, we will need learning machines in every pocket. Companies such as Facebook and Google are barely scratching the surface. “There’s a huge haul of data banging on their door without them being able to make sense of it,” says Carron.

Editor’s note: Original Source: ‘NewScientist’

This article appeared in print under the headline “Making light work of AI”

Hal Hodson. “Move over silicon: Machine learning boom means we need new chips”

NewScientist. N.p., Web. 24 August. 2016.

In 2016, government systems integrators continue to battle a wide-range of margin-squeezing challenges that stem from decreased federal spending.

They are tasked with developing demanding next-generation solutions in the mobile, big data and cloud computing areas.  However, it is often difficult to deliver acceptable technology solutions within budget.

The core issue is that it involves a significant investment to develop customized solutions and systems tailored to unique program requirements.  Systems integrators and their customers need to employ technical advantages that enable them to solve problems and implement field advanced technology at a similar or lower level of effort.

Fortunately, the pace of commercial innovation is such that opportunities exist for systems integrators that were not even options in the very recent past.  They can now can leverage tools such as automated application factories that produce customizable mobile applications for a fraction of the investment in coding and development required in the past.

In fact, these low-code and zero-code solutions allow companies to rapidly build and deploy fully customized applications that are tailored to meet the unique business and workflow requirements of government. End users, without software or engineering training, can literally create mobile apps with custom forms, maps and features – all from a simple, graphical interface.

This is not just a modest improvement of the status quo; rather it is a completely disruptive innovation that dramatically lowers the cost of fielding high-end, tailored software solutions.

Enterprises can now build apps without requiring the expertise, expense and ongoing maintenance of commercial software.  Also, for service providers, it is possible to develop and private-label these apps in ways that demonstrate premium brand value without investing in mobile app development services or staff.

Government IT continues to face budget scrutiny at a time when their innovations are most needed for mission success. These new zero code applications allow the customer to rapidly build iOS, Android and web apps that are fully-customized to meet any need.

Zero code apps go beyond the “low code” platforms, which are becoming more common in the corporate enterprise space – especially for business process management (BPM) solutions. The challenge with these “low-code” applications is that they still require a level of software and engineering expertise to enable “citizen developers.”  Conversely, zero code applications literally do not require any coding and can be built by end users.

Of course, there will always be situations where more complex capabilities are required and extend outside the existing feature set available from zero code platforms.  But now for time being, we have limited the scope of systems integration and isolated engineering effort (man-hours and budget) to only those areas.   Further, as these new zero code apps continue to expand the catalog of available features, the adaptation and customization costs will continue to shrink.

Ultimately, by offering these types of zero-code applications as part of technical solutions, we can help the government customer and the system integrator.  Government stakeholders and end users get the fully-customized application they need. The IT department and the systems integrator become heroes, delivering solutions at a fraction of the cost of traditional software development.

In the end, everyone truly wins.

Editor’s note: Original Source: ‘Washington Technology’

John Timar. “Get ready for the rising tide of zero-code development”

Washington Technology. N.p., Web. 4 August. 2016.