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Pattern recognition carves smooth road for Big Data

Posted: 18 Feb 2015 ?? ?Print Version ?Bookmark and Share

Keywords:NeuroMem? big data? analytics? IoT? pattern matching?

The need for effective data collection and interpretation technologies has never been greater. Especially now that increasingly large volumes of data are being generated across IoT, it is becoming more and more essential to have an efficient system for collecting and interpreting data. In fact, the pervasiveness of Big Data has already crossed over to the mainstream to become 'objects' of interest in various television programs and movies.

If you've ever seen the U.S. TV series "Person of Interest," during which an anonymous face in the Manhattan crowd, highlighted inside a digital frame, is identified by "The Machine" as the victim (or perpetrator) of an impending crime, you get the picture: pattern recognition, the heart of 'Big Data' analytics, is part of everybody's future.

Scene from Person of Interest

Scene from Person of Interest

The TV series, although a fictional drama, reveals a real-life escalation in the era of massive data collection.

IoT, Big Data or Big Brother, "the buzzwords are all about data collection and data interpretation," stated Philippe Lambinet, former STMicroelectronics EVP.

Analytics is necessary so that [a machine] can make relevant decisions. "Data collection is becoming so enormous that we need new ways of doing data analytics," he said.

Lambinet, now the acting CEO of a start-up called NeuroMem, is betting his farm on the growing demand for pattern recognition in data analytics.

Unlike established computing architectures, which Lambinet thinks are too slow and too power hungry for pattern matching, NeuroMem, a fabless chip company, is promising to enable efficient pattern recognition by bringing massively parallel, low-power, real-time, cost-effective computing architecture with a built-in capability to learn on the fly.

Pattern-matching architectures

Comparing two pattern-matching architectures: Sequential vs. Parallel (Source: NeuroMem)

Most people implementing pattern matching algorithms use the traditional computing model also called the von Neumann architecture, said Lambinet.

They implement, in software, a fundamentally parallel algorithm and then they run it on a fundamentally sequential hardware. He thinks the need for analysing massive data is changing that landscape and creating serious demands for non-von Neumann computing architecture.

Lambinet believes the start-up will be able to raise enough money, about $5 million, by the end of this month to get its business going.

Five mil doesn't sound like a lot of money for a fabless semiconductor start-up.

But the nice thing about NeuroMem, said Lambinet, is that "we are leveraging the work already done by General Vision." General Vision, a Petaluma, Calif. company founded 27 years ago, has long specialised in image processing technology for machine vision, and image recognition systems based on neural networks.

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