The Titan M Chip Powers Up Pixel 3 Security


The Google Pixel 3 has all the betterments you would expect from a flashy flagship smartphone: great camera, zippy processor, smarter AI. It also, though, comes with an unexpected bonus, one that works so deeply in the background you'll likely never even know it's there. The Titan M chip may be small and discreet, but it helps make the Pixel 3 and its beefier sibling, the Pixel 3 XL, among the most secure smartphones you can buy. The Titan M draws inspiration from the Titan chip that helps safeguard Google servers, and while they differ some in the details--the Titan M draws much less power, for instance, so as not to tax your battery--they both share the task of protecting hardware against the most sophisticated, and devastating, attacks. And because it sits entirely apart from the Pixel 3's main processor, it helps cordon off the most sensitive data your smartphone holds.

Are Silicon Valley's top companies running out of innovative business ideas?


Recent news that Apple and Google's autonomous auto projects are in trouble could indicate a troubling dearth of innovative business ideas from two of Silicon Valley's largest and most iconic companies. Jake Smith's ZDNet story, "Apple car plan hits snag..." reports on information acquired by the New York Times and Financial Times that there have been "dozens" of layoffs and high-level executive departures from Apple's secret Project Titan. Alistair Barr's Bloomberg story, "Google's Self-Driving Car Project Is Losing Out to Rivals," points to serious challenges facing Google's car project, which still lacks much needed partnerships. The Apple and Google automobile projects have gathered tremendous amounts of attention from the public and from within the companies. The New York Times reports that over an 18 month period Apple grew Project Titan to more than 1,000 staff.

AMD chases the AI trend with its Radeon Instinct GPUs for machine learning


With the Radeon Instinct line, AMD joins Nvidia and Intel in the race to put its chips into AI applications--specifically, machine learning for everything from self-driving cars to art. The company plans to launch three products under the new brand in 2017, which include chips from all three of its GPU families. The passively cooled Radeon Instinct MI6 will be based on the company's Polaris architecture. It will offer 5.7 teraflops of performance and 224GBps of memory bandwidth, and will consume up to 150 watts of power. The small-form-factor, Fiji-based Radeon Instinct MI8 will provide 8.2 teraflops of performance and 512GBps of memory bandwidth, and will consume up to 175 watts of power.

Google touts Titan security chip to market cloud services

Daily Mail - Science & tech

Alphabet Inc s Google this week will disclose technical details of its new Titan computer chip, an elaborate security feature for its cloud computing network that the company hopes will enable it to steal a march on Titan is the size of a tiny stud earring that Google has installed in each of the many thousands of computer servers and network cards that populate its massive data centers that power Google's cloud services. Google is hoping Titan will help it carve out a bigger piece of the worldwide cloud computing market, which is forecast by Gartner to be worth nearly $50 billion. Titan is the size of a tiny stud earring that Google has installed in each of the many thousands of computer servers and network cards that populate its massive data centers that power Google's cloud services. Titan scans hardware to ensure it has not been tampered with.

Baidu Releases AI Benchmark EE Times


DeepBench is available online along with first results from Intel and Nvidia processors running it. The benchmark tests low-level operations such as matrix multiplication, convolutions, handing recurrent layers and the time it takes for data to be shared with all processors in a cluster. Machine learning has emerged as a critical workload for Web giants such as Baidu, Google, Facebook and others. The workloads come in many flavors serving applications such as speech, object and video recognition and automatic language translation. Today the job of training machine learning models "is limited by compute, if we had faster processors we'd run bigger models…in practice we train on a reasonable subset of data that can finish in a matter of months," said Greg Diamos, a senior researcher at Baidu's Silicon Valley AI Lab.