The Role of Data Scientists, for Today and Tomorrow

Author: R M Ramanathan

A Perspective


What we commonly label as “disruptive” is often the result of breaking the status quo. Disruptions seem to have their cycles and a few important ones come to my mind. In 1800s, we invented steam engines, cracked the math of thermodynamics, giving way to a number of derived industries: factories, locomotives, mills, etc. The lifecycle of this innovation reached its destiny sixty years later with the first ever depression. People could have been smarter and learned their lesson. But they became smarter by inventing Electricity and Magnetism just eight decades later (1880s) than the invention of steam engines. That disrupted status quo and rapid electrification took place across the world. Another eight decades later (1960s) we got transistors, computers and the beginnings of the internet. Six decades since then (2020s), we are in the cusp of another cycle. The sure candidate for next disruption is AI along with bio/nano technology.

I notice a pattern here. All disruptions happen when the previous one is mastered, done and dusted. Today we are within arms length of reaching the limits of Moore’s Law. The situation is primed for the next disruption. Amidst the evolution on doubling computing power every year and a half, the silent winner has been an exponential increase of data over the same period. Every computerized establishment saved cost, did more of the same (old) business in the same business year. Well, we homo sapiens, are too smart to run same (old) businesses. We leveraged data for intelligence and built new markets, new products and new strategies. In the last 2-3 decades of the recent technological cycle, we have progressed enough on big data technologies to enable the next disruption.

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Edge Computing in a Telecom Network

Author: Arvind Padmanabhan

Powered by Multi-access Edge Computing (MEC)


Until a few years ago, for many decades, telecom operators had been claiming that all they provided was a “dumb pipe”. It was not their concern what those pipes carried; and they were not responsible for content that was served or consumed at both ends of the pipe. Their main goal was to ensure that the pipes had enough bandwidth to serve average and peak traffic requirements. All was well and good until over-the-top (OTT) content starting dominating the market. Such content bypassed legacy circuit-switched services and value-added services provided by the operators.

Indeed telecom operators started to lose both ends of the bargain. They now wanted control over the content and charge a premium but all they could collect from their customers was for bare bits and bytes. In a price-sensitive market, “unlimited plans” became the order of the day. When OTT content became predominant, operator revenues started to drop. Content also became rich in multimedia, particularly video that congested backhaul links. Operators couldn’t afford to upgrade these backhauls. Yes, operators were providing dumb pipes but apparently even these had a hard time keeping up with demand.

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SoC industry dynamics beyond the smartphone era

Author: Prakash Mohapatra

Industry analysis and opinion


Smartphones play a vital role in our life; it is sort of panacea for us. It has achieved massive growth in the last decade. With such demand of the end product, it is obvious that the supply side has worked in overdrive. Semiconductor industry has seen massive growth due to smartphones. The number of OEMs increased to fulfil the volume demands, pulling more vendors into semiconductor industry to suffice the OEMs’ supply chain requirements.

A few OEMs made fortune in smartphone sales, with revenue exceeding billions cumulatively. Such capital inflow encouraged them to invest more money on product development, to fulfil the end users’ paradoxical requirement of more performance with more battery life (less power consumption) at a lower price. With cumulative silicon sales going into billions, the semiconductor industry responded positively, and focused on stretching the innovation into leading process nodes and other techniques to enhance performance.

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When Machines Begin To Talk

Author: Arvind Padmanabhan

An opinion


If there’s one thing that’s unique about being human, we can’t deny that it’s speech. Sure, animals have a way of communicating but that’s not in any way as refined as we humans do. The key lies in the way we are able modulate sound waves with the rolls and wags of our tongue. Perhaps, it’s for this reason we have phrases such as “mother tongue”. Aural communication in the rest of the animal kingdom is limited to coarse sounds that we simply name as moos, grunts and hee-haws. But it’s not going to be long before we lose our monopoly of speech.

The best machines could do in the past were beeps and alarms. Those of us who have lived through the times of early fixed line modems can recall the staccato of beeps as they tried to handshake and establish a connection to a server somewhere on the internet. Of course, machines also talk among themselves silently, by parsing bits and bytes. But now the time has come for machines to talk directly to humans via human speech.

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Why, How and What of Custom SoCs

Author: Prakash Mohapatra

An opinion


Ever watched the Ted talk by Simon Sinek, How great leaders inspire actions? Not yet? Then I encourage you watch this 20-minute talk. This video covers the most fundamental thing that most companies fail to address: connecting with customers! Often companies focus on their products, going into details about the technical features, price, engineering innovation, etc. However, they fail to address the basic thing that is needed for a successful sale: Why they are offering the product? Answering this question bridges the gap between product and market. Revenue is an outcome, not the sole purpose of a company’s existence.

Let us take an example of a conventional sales pitch for the embedded computing platform: System on Module (SoM).

“We offer SoM that has a SoC, memory, power circuitry, Operating System, and BSPs, all integrated on a small form-factor board that offers you a platform for building your next embedded product”.

Sounds exciting? Well, it depends. However, it does not generate a great interest. Now, how about the following as a sales pitch? Continue reading “Why, How and What of Custom SoCs”

RISC-V: Open Source Instruction Set Architecture

Author: Prakash Mohapatra

The new kid on the block


The emerging IoT industry is an aggregation of products and services, complementing each other to enable efficiency and cost optimization in multiple industries. It does not have a vertically oriented value chain. IoT end nodes will be scattered in billions in various industries.

As mentioned in my earlier post ARM vs Intel: The new war frontiers, COTS processors will not be ideal for building these end nodes, as the latter are application specific. Companies would be inclined to adopt custom processors as they offer flexibility to assemble only required parts. These parts can include analogue sensor, DSP, proprietary IP, etc. Further, custom processors substantially reduce BoM cost and die size, which will minimize power dissipation. It also helps companies to differentiate their product from those of their competitors. In view of failing Moore’s Law, customization is the answer as it can reduce the BoM cost significantly.

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Artificial Intelligence: The Time Is Now

Author: Arvind Padmanabhan

Takeaways from Intel AI Developer Workshop @ Bangalore

intel-aiImages in this article are copyright of Intel.

I just returned from a full-day developer workshop organized by Intel. The focus was on Artificial Intelligence (AI) and how Intel is contributing in mankind’s best efforts to teach machines to sense, reason and decide. The event was a useful peek into what Intel is bringing to the table in terms of both hardware and software. Beyond that, it was not an event that I would call a developer workshop since there were no hands-on sessions, demos or even tips and tricks that developers can use. The event was structured as a line of talks in order to bring awareness of Intel’s involvement in AI and where the market is headed.

AI originated in the 1950s but it was only in the 1980s when Machine Learning (ML) came about that people started to think it might be possible to realize AI. Machines can be trained and then asked to solve problems. Their algorithms could be tweaked as they learn and relearn with more sets of data. Deep Learning (DL) came about as a sub-branch of ML, where neural networks became the basis of learning. DL has brought us closer to the dream of realizing AI but DL alone did not achieve this.

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