KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

By John Russell

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. It’s nice to have patient capital, a rare commodity for startups these days. The company contends its KNUPATH Hermosa processor with 256 DSP cores and its Lambda fabric will bring performance, scalability, energy, and programmability advantages over CPUs, GPUS, and FPGAs to a wide swath of machine learning applications. The first commercial boards – code named Mavericks – are expected around March this year.

Founded in the 2005 timeframe by Daniel Goldin, the long time NASA administrator, KnuEdge has raised roughly $100M no doubt stemming from investor confidence in Goldin’s extensive technology creation and delivery history. Goldin and company believe their investors’ patience is about to start paying off. KnuEdge has two business units, KNUPATH focused on hardware accelerators based on Hermosa and Lambda technology, and KnuVerse, focused on voice and face recognition systems. The latter, said Steve Cumings, CMO, KnuEdge, has customers in the government sector. Company revenues are somewhat north of $20 million so far.

Broadly, KnuEdge’s view is that a highly scalable processor in a single socket is handicapped in addressing growing machine learning and large-scale computing challenges. In contrast, the company’s Lambda Fabric enables a large number of “KNUPATH Hermosa processors to be interconnected in low latency, high throughput mesh for massively parallel processing which is well suited for application needs that will drive the compute engines of the future.”

This isn’t exactly a new idea. The Hermosa chip and Lambda technology will enter the market amid a gush of machine learning technologies all striving to advance data-driven science and enterprise data analytics. Indeed the emergence of heterogeneous computing architectures relying on a variety of accelerator engines is a key feature of today’s computing landscape. Given Goldin’s remarkable achievements at NASA it should be interesting to watch KnuEdge’s progress.

Early developer boards with two Hermosa chips have been available for some time. Volume sales of individual chips are planned to begin in January followed by the Mavericks offering, a PCIe board with four Hermosa chips, towards the end of the quarter.

Presented as a “neural computing” approach, the KNUPATH architecture actually attempts to mimic nervous system communication more than brain-inspired spiky neuron ‘inference logic’ (discussed further below).

Patrick Patla, senior vice president and general manager of KNUPATH and a former AMD executive, said, “What’s unique about Hermosa’s 256 DSP cores is that they are hooked together at a central part of the processor with a router that has 16 ports. Using the Lambda fabric, it’s possible, at least theoretically, to scale to 500,000 Hermosa processors.

“We are a data flow machine. So you push data through the system and can have the calculation and different algorithms change on the fly. We are different than a GPU accelerator in that they use a SIMD architecture. We use multiple programs, multiple data, so on our 256 cores we could have 256 separate algorithms running. You would push data through those algorithms and then you have hits on the data at different hit rates based on the algorithms and you can tune and resend algorithms to those DSPs through packets,” explained Patla.

“Basically the packets that we send through the Lambda network are what allows the programming of the DSP, so packets deliver the program, the algorithm, and then bring the payload, and push the data through it. Not only are you getting all the data and the operating instructions with each packet, but each core also knows the next destination for that information so it’s extremely efficient.” One result is very low latency at various systems levels (see diagram below).

Patla also contrasted Hermosa’s ease of use with emerging brain-inspired neuromorphic chips such as IBM’s TrueNorth, which uses “spiking neuron” architecture.

“Spiky algorithms are notoriously difficult to program. Commonly they are trained on other networks first and then moved onto the neuromorphic chip so the actual software side of that is different,” he said.

As noted earlier the Hermosa-Lambda architecture emulates neuronal connectivity more than brain processing. “If you look at the different neuron-based approaches, our inspiration really gives you lots of little engines – that’s the background of the DSP cores, what we affectionately sometimes call tDSPs or tiny DSPs,” said Patla. Reliance on familiar DSP architecture eases programming.

“Our tools sit on a C/C++ library set on top of LLVM (compiler). And everybody is familiar with OpenCL as well as OpenMPI which is very comfortable in our architecture,” said Patla. The Hermosa/Lambda architecture also supports NUMA (non uniform memory access) and each processor has memory directly (72MB) on it. “Much of the advantage is the dataflow but also all the advantages of common programming techniques for anybody that has worked on OpenMPI. Many of the other [neuromorphic] architecture require a different set of tools.”

Hermosa Development Board

KnuEdge has had a software developer kit out for “quite some time” and it is already in the hands of many developers, according to Patla.

It all sounds great. In April KnuEdge will hold a Hermosa developers’ conference at UCSD as well as a “heterogeneous neural network conference” in partnership with UCSD for the development of next generation algorithms that can take advantage of new architectures such as Hermosa. Patla said performance benchmarks for chip will be forthcoming with the release of the commercial product; it seems like the developer conference would be a good place to do so, but he wouldn’t specify when beyond the first half of the year.

“Right now, as you would imagine, we are in the labs with our SDKs and final verification of those commercial systems as we are tuning and bringing all of our code to the processors. In the future we’ll show configurations of 4, 8, 12, 16, Hermosas together to show the scalability of the Lambda fabric. When Steve talked about mimicking the nervous systems it really is about our connectivity and the fact that when you add more Hermosas to the network, we continue to scale because with every socket you are adding more memory as well. Each processor has 72MB of onchip memory that is sufficient for the programming of our kinds of algorithms and the workload we are trying to tackle.”

Currently the chip is being fabbed by GLOBALFOUNDARIES on the 32nm process. “It’s a well behaved chip where these 256 cores and fabric and everything lives in a 35-watt part,” said Patla.

The KNUPATH folks believe Hermosa has the potential to meet a wide variety of machine-learning kinds of applications performed in heterogeneous computing environments as well as an opportunity to replace existing approaches to those applications.

‘We have a demo on the website that compares us to the most current NVIDIA card and we have a 2.5x performance. It is very interesting that a video card isn’t very good at video compression that we are good at because of the parallelism of communication we handle across the memory. So that’s one of the spaces we’ll be aiming at. And of course it will also find its way into many of the single board computer spaces because at 35 watts and the ability to do signal processing and such fine grained computing we actually expect it to replace many FPGAs in a lot of environments.”

Patla argues Hermosa/Lambda’s flexibility is a major benefit and door opener – one could divvy the chips up and have a multipurpose SOC instead of dedicating it to just one task. He used a video analysis application as an example of flexibility and reprogrammability.

“You can reprogram a core by just delivering a new packet. For example, if you were doing video analysis and were searching within videos, you could be looking for ball caps. You could have all the different algorithms looking at ball caps and you could just all of a sudden reprogram and divide the chip and have 25 percent of the chip looking for red ball caps and 25 percent looking for blue caps. You could flip to four different algorithms in nanoseconds. Then when you have high hit rates and you realize the one you are really looking for, and you could say OK now all care about our green ball caps and that algorithm would propagate against all the cores and you’d be able to take your throughput up. It’s very fast, very flexible,” he said.

At SC16, the KNUPATH team was busily evangelizing. Patla said they talked to a number of cloud providers as well as national labs that expressed interest to the point that he is expecting some new workloads to emerge.

There’s still much to do. Patla ticked off desirable milestones for 2017 – getting out of the lab, showcasing a couple of commercial customers and workloads, integrating the many machine learning frameworks, making sure Hermosa-based systems get into the cloud somewhere for development and production purposes, to name but a few.

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!

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody would like more juice to run compute-intensive HPC simulatio Read more…

By Alex Woodie

Is Data Science the Fourth Pillar of the Scientific Method?

April 18, 2019

Nvidia CEO Jensen Huang revived a decade-old debate last month when he said that modern data science (AI plus HPC) has become the fourth pillar of the scientific method. While some disagree with the notion that statistic Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing the bounds of what's possible in business and science, in w Read more…

By Alex Woodie with Doug Black and Tiffany Trader

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Bridging HPC and Cloud Native Development with Kubernetes

The HPC community has historically developed its own specialized software stack including schedulers, filesystems, developer tools, container technologies tuned for performance and large-scale on-premises deployments. Read more…

Google Open Sources TensorFlow Version of MorphNet DL Tool

April 18, 2019

Designing optimum deep neural networks remains a non-trivial exercise. “Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expens Read more…

By John Russell

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the Read more…

By HPCwire Editorial Team

Intel Gold U-Series SKUs Reveal Single Socket Intentions

April 18, 2019

Intel plans to jump into the single socket market with a portion of its just announced Cascade Lake microprocessor line according to one media report. This isn Read more…

By John Russell

BSC Researchers Shrink Floating Point Formats to Accelerate Deep Neural Network Training

April 15, 2019

Sometimes calculating solutions as precisely as a computer can wastes more CPU resources than is necessary. A case in point is with deep learning. In early stag Read more…

By Ken Strandberg

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

Nvidia Doubles Down on Medical AI

April 9, 2019

Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery. The GPU leader said Monday it will collaborate with the American College of Radiology (ACR) to provide clinicians with its Clara AI tool kit. The partnership would allow radiologists to leverage AI techniques for diagnostic imaging using their own clinical data. Read more…

By George Leopold

Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions

April 9, 2019

With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workfl Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This