Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

By Alex Woodie

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads.

Google has been using its TPUs for the inference stage of a deep neural network since 2015. It credits the TPU for helping to bolster the effectiveness of various artificial intelligence workloads, including language translation and image recognition programs. It also says TPU helped power its widely reported victory in the game of Go.

While TPUs aren’t new to Google data centers, the company started talking about them publicly only recently. Earlier this month, the Alphabet subsidiary opened up about the TPU, which it called “our first machine learning chip,” in a blog post. The company also released a technical paper, titled “In-Datacenter Performance Analysis of a Tensor Processing Unit​,” that details the design and performance characteristics of the TPU.

According to the paper, Google’s TPU was 15 to 30 times faster at inference than Nvidia’s K80 GPU and Intel Haswell CPU in a Google benchmark test. On a performance per watt scale, the TPUs are 30 to 80 times more efficient than the CPU and GPU (with the caveat that these are older designs). You can read more details on the TPU comparisons here.

While Google has been mum on possible commercial ventures around the TPU, some recent developments indicate that Google itself may not be aiming to compete directly with traditional chip manufacturers. Last week CNBC reported that a group of the original Google engineers who designed the TPU recently left the Web giant to found their own company, called Groq.

Google’s TPU chip (Source: Google)

According to an SEC document filed for Groq’s incorporation, the company has raised about $10 million. Leading the way is Chamath Palihapitiy, a prominent Silicon Valley venture capitalist. Other ex-Googlers named in the SEC document include Jonathan Ross, who helped invent the TPU, and Douglas Wightman, who worked on the Google X “moonshot factory.”

But that’s not all. “We have eight of the 10 original people that built that chip building the next generation chip now,” Palihapitiy said in a March interview with CNBC. Groq is playing its cards close to the vest, and isn’t disclosing exactly what it’s working on—although by all indications, it would appear to have something to do with machine learning chips.

There are many other groups chasing this new market opportunity, including traditional chip bigwigs Intel and IBM.

While Big Blue pushes a combination of its RISC Power chips and Nvidia GPUs in its Minsky AI server, its research arm is exploring other chip architectures. Most recently, the company’s Almaden Lab has discussed the capabilities of its “brain-inspired” TrueNorth chip, which features 1 million neurons and 256 million synapses. IBM says TrueNorth has delivered “deep networks that approach state-of-the-art classification accuracy” on several vision and speech datasets.

“The goal of brain-inspired computing is to deliver a scalable neural network substrate while approaching fundamental limits of time, space, and energy,” IBM Fellow Dharmendra Modha, chief scientist of Brain-inspired Computing at IBM Research, said in a blog post.

Intel isn’t standing still, and is developing its own chip architectures for next-generation AI workloads. Last year the company announced that its first AI-specific hardware, code-named “Lake Crest,” which is based on technology Intel acquired with $400-million acquisition of Nervana Systems, would debut in the first half of 2017. That is to be followed later this year with Knights Mill, the next iteration of its Xeon Phi co-processor architecture.

IBM’s TrueNorth training set (image source: IBM Research)

For its part, Nvidia will be looking to solidify its hold on the emerging machine learning market. While energy-hungry GPUs aren’t as efficient on the inference side of the equation, they’re tough to be beat for the compute-intensive training of neural networks, which is why Web giants like Google, Facebook, Microsoft and others are using so many of them for AI workloads.

However, Nvidia isn’t giving up on the inference side of the market, and recently published a benchmark that showed how much better its latest Pascal GPU architectures, most notably the P40, is at inferring than its older Kepler GPU architecture (see HPCwire’s coverage here). The K80 also out-performed the Google TPU, although Google has probably advanced its TPU since 2015, which is when it calculated the benchmark figures it recently shared. Nvidia’s recent hiring of Clément Farabet (formerly of Twitter) also could also portend a shift to more real-time workloads too.

Qualcomm could also be involved in the inference side of the equation. The mobile chipmaker has been working with Yann LeCun, Facebook’s Director of AI Research, to develop new chips for real-time inference, according to this Wired story. LeCun developed one of the first AI-specific chips for inference more than 25 years ago while working at Bell Labs.

The San Diego company recently announced plans to spend $47 billion to buy NXP, a Dutch company that makes chips for cars. NXP was working on deep learning and computer vision problems before the acquisition was announced, and it appears that Qualcomm will be looking to NXP to give it an edge in developing systems for autonomous driving.

Self-driving cars are one of the most prominent areas where deep learning and AI will have an impact. Beyond that, there are many other places where having an on-board AI chip to react to real-world conditions, including in mobile phones and virtual reality headsets. The technology is moving very quickly at the moment, and we’ll soon see other practical uses that will impact our lives.

This article first appeared on our sister site, Datanami.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Glimpses of Today’s Total Solar Eclipse

August 21, 2017

Here are a few arresting images posted by NASA of today’s total solar eclipse. Such astronomical events have always captured our imagination and it’s not hard to understand why such occurrences were often greeted wit Read more…

By John Russell

Tech Giants Outline Battle Plans for Future HPC Market

August 21, 2017

Four companies engaged in a cage fight for leadership in the emerging HPC market of the 2020s are, despite deep differences in some areas, in violent agreement on at least one thing: the power consumption and latency pen Read more…

By Doug Black

Geospatial Data Research Leverages GPUs

August 17, 2017

MapD Technologies, the GPU-accelerated database specialist, said it is working with university researchers on leveraging graphics processors to advance geospatial analytics. The San Francisco-based company is collabor Read more…

By George Leopold

HPE Extreme Performance Solutions

Leveraging Deep Learning for Fraud Detection

Advancements in computing technologies and the expanding use of e-commerce platforms have dramatically increased the risk of fraud for financial services companies and their customers. Read more…

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Centers (IPCCs) has resulted in a new Big Data Center (BDC) that Read more…

By Linda Barney

Tech Giants Outline Battle Plans for Future HPC Market

August 21, 2017

Four companies engaged in a cage fight for leadership in the emerging HPC market of the 2020s are, despite deep differences in some areas, in violent agreement Read more…

By Doug Black

Microsoft Bolsters Azure With Cloud HPC Deal

August 15, 2017

Microsoft has acquired cloud computing software vendor Cycle Computing in a move designed to bring orchestration tools along with high-end computing access capabilities to the cloud. Terms of the acquisition were not disclosed. Read more…

By George Leopold

HPE Ships Supercomputer to Space Station, Final Destination Mars

August 14, 2017

With a manned mission to Mars on the horizon, the demand for space-based supercomputing is at hand. Today HPE and NASA sent the first off-the-shelf HPC system i Read more…

By Tiffany Trader

AMD EPYC Video Takes Aim at Intel’s Broadwell

August 14, 2017

Let the benchmarking begin. Last week, AMD posted a YouTube video in which one of its EPYC-based systems outperformed a ‘comparable’ Intel Broadwell-based s Read more…

By John Russell

Deep Learning Thrives in Cancer Moonshot

August 8, 2017

The U.S. War on Cancer, certainly a worthy cause, is a collection of programs stretching back more than 40 years and abiding under many banners. The latest is t Read more…

By John Russell

IBM Raises the Bar for Distributed Deep Learning

August 8, 2017

IBM is announcing today an enhancement to its PowerAI software platform aimed at facilitating the practical scaling of AI models on today’s fastest GPUs. Scal Read more…

By Tiffany Trader

IBM Storage Breakthrough Paves Way for 330TB Tape Cartridges

August 3, 2017

IBM announced yesterday a new record for magnetic tape storage that it says will keep tape storage density on a Moore's law-like path far into the next decade. Read more…

By Tiffany Trader

AMD Stuffs a Petaflops of Machine Intelligence into 20-Node Rack

August 1, 2017

With its Radeon “Vega” Instinct datacenter GPUs and EPYC “Naples” server chips entering the market this summer, AMD has positioned itself for a two-head Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Leading Solution Providers

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This