‘Biomolecular Motor-based’ Computer Promises Speed and Reduced Power

By John Russell

March 2, 2016

Combinatorial tasks are among the hardest for traditional computers. A good example is finding the optimum path through a large complicated network. Every possible path must be evaluated and as datasets grow the computing time grows exponentially making some tasks unfeasible. One practical example is verification of VLSI (very large scale integrated) semiconductor circuit design. Indeed, many VLSI circuit designs are never ‘fully verified’ because the combinatorial calculation is prohibitively time-consuming and instead rely on approximations.

Last week, a group of international researchers brought a new approach to the combinatorial problem processing challenge, which they say is the world’s first “biomolecular motor-based” parallel computer. Not only does it solve combinatorial problems much faster but also with orders of magnitude less power consumption than comparable electronic computers, they report.

“Electronic computers are extremely powerful at performing a high number of operations at very high speeds, sequentially. However, they struggle with combinatorial tasks that can be solved faster if many operations are performed in parallel. Here, we present proof-of-concept of a parallel computer by solving the specific instance {2, 5, 9} of a classical nondeterministic-polynomial-time complete (“NP-complete”) problem, the subset sum problem,” write the authors in their paper, “Parallel computation with molecular-motor-propelled agents in nanofabricated networks”[i], published in PNAS last week.

So what is a biomolecular motor-based parallel computer?

Microtubules at a junction
Microtubules at a junction

The researchers take components of a typical cell – tiny microtubule filaments that are normally part of a cell’s cytoskeleton and motor proteins that do the pushing and pulling – and enter them into a network of microchannels whose geometry is a ‘computing’ machine. As the microtubules flow through the network, they are directed by ‘gates’, which perform a kind of addition. (see diagram on the side).

Conceptually, the approach involves, “[E]ncoding combinatorial problems into the geometry of a physical network of lithographically defined channels, followed by exploration of the network in a parallel fashion using a large number of independent agents, with very high energy efficiency….Our approach replaces the requirement for exponentially growing time needed by traditional, electronic computers to solve NP-complete problems, with the requirement for an exponentially growing number of independent computing agents [microtubules].”

The new work is from researchers whose various affiliations include UC Berkeley, Lund University, Technische Universitat, Max Planck Institute, Linneaus University, McGill University, and the University of Liverpool (authors listed at end of article). Their proof of concept work, spelled out in more detail below, could have major impact on efforts to solve many combinatorial tasks besides circuit design verification, such as protein folding an design, and optimal network routing.

“Our approach has the potential to be general and to be developed further to enable the efficient encoding and solving of a wide range of large-scale problems. Accomplishing this would move forward (but not remove) the limit of the size of combinatorial problems that can be solved,” contend the authors.

There have been many efforts to develop and apply novel computing architectures to the combinatorial calculation problem. DNA computing and quantum computing come to mind. The authors note most of the new approaches have significant drawbacks:

  • DNA computation, which generates mathematical solutions by recombining DNA strands, or DNA static or dynamic nanostructures, is limited by the need for impractically large amounts of DNA.
  • Quantum computation is limited in scale by decoherence and by the small number of qubits that can be integrated.
  • Microfluidics-based parallel computation is difficult to scale up in practice due to rapidly diverging physical size and complexity of the computation devices with the size of the problem, as well as the need for impractically large external pressure.

The bio-molecular motor method, argue the authors, overcomes most challenges and has many benefits not least much improved heat dissipation characteristics, “the approach demonstrated here consumes orders of magnitude less energy per operation compared with both electronic and microfluidic computers.”

Here is a bit more detail on how the computation is done. The channel-guided unidirectional motions of agents are equivalent to elementary operations of addition, and their spatial positions in the network are equivalent to ‘running sums.’ Starting from an entrance point at one corner of the network agents are guided downward by the channels in vertical or diagonal directions.

Encoding of the combinatorial Subset Sum Problem into a lithographically defined network of nanoscale channels – green numbers label the problem’s solutions at the network’s exits.
Encoding of the combinatorial Subset Sum Problem into a lithographically defined network of nanoscale channels – green numbers label the problem’s solutions at the network’s exits.

Two types of junctions were designed to regulate the motion of agents in the network: “split junctions,” where agents are randomly distributed between two forward paths, and “pass junctions,” where agents are guided onward to the next junction along the initial direction. The vertical distance (measured as the number of junctions) between two subsequent rows of split junctions represents an integer from the set S.”

After traversing the network, the filaments emerge at exits corresponding to the target sums and are either recycled back to the entrance point or collected. The channel networks were fabricated by electron-beam lithography on SiO2 substrates to obtain the required resolution and fidelity.

Minimizing computation errors is also an important component; the error rates of microtubules flow at pass junctions must be kept as low as possible. In the proof-of-concept experiment, the results were promising. Statistical analysis of the motion of actin filaments and the microtubules showed that 97.9% and 99.7%, respectively, took the correct (straight) paths through pass junctions, whereas split junctions distributed filaments approximately evenly experimental data are in good agreement with those obtained by Monte Carlo simulations.

The details of the process are best taken directly from the paper, which has multiple figures.

There are certainly challenges beyond the reported POC work to make biomolecular motor-based computing practical. The authors note six:

  1. Scaling up of the physical network size from currently “100 × 100 μm2 to wafer size, which is achievable by current patterning technology.
  2. Reduction of the filament feeding time, which can be achieved by using networks with multiple entrances, or by self-replicating filaments.
  3. Reduction of pass-junction error rates, which can be realized by simulation-driven design by evolutionary algorithms for designing the junction geometries or by using 3D geometries such as bridges or tunnels which would offer zero error rates at pass junctions.
  4. To circumvent the inherent difficulties of tracking large numbers of individual filaments, automatic readout schemes at exits of interest will likely be required.
  5. Programmable devices which can flexibly encode different problems could be achieved by using heat-controlled or electrostatic gates in only one programmable type of junction instead of the two (isomorphic) static junctions.
  6. Filaments can be prevented from attaching to or detaching from the network by using closed channels with porous openings for allowing the supply of ATP.

[i] Parallel computation with molecular-motor-propelled agents in nanofabricated networks, Proceeding of the National Academy of Science; Dan V. Nicolau Jr., Mercy Lard, Till Kortend, Falco C. M. J. M. van Delftf, Malin Perssong, Elina Bengtssong, Alf Månssong, Stefan Diezd,e, Heiner Linkec, and Dan V. Nicolau; http://www.pnas.org/content/early/2016/02/17/1510825113.abstract

Image Credits: Till Korten, B CUBE; Mercy Lard, Lund University; Falco van Delft, Philips Research

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!

RIKEN Post-K Supercomputer Named After Japan’s Tallest Peak

May 23, 2019

May 23 -- RIKEN President Hiroshi Matsumoto announced that the successor to the K computer will be named Fugaku, another name for Mount Fuji, which is the tallest mountain peak in Japan. Supercomputer Fugaku, developed b Read more…

By Tiffany Trader

Cray’s Emerging Market & Technology Director Arti Garg Peers Around HPC/AI Corner

May 23, 2019

In her position as emerging market and technology director at Cray, Arti Garg doesn't just have a front-row seat to the future of computing, she plays an active role in making that future happen. Key to Garg's role is understanding how deep learning scientists are using state-of-the-art HPC infrastructures and figuring out how to push those limits further. Read more…

By Tiffany Trader

Combining Machine Learning and Supercomputing to Ferret out Phishing Attacks

May 23, 2019

The relentless ingenuity that drives cyber hacking is a global engine that knows no rest. Anyone with a laptop and run-of-the-mill computer smarts can buy or rent a phishing kit and start attacking – or it can be done Read more…

By Doug Black

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.

For 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

Smarter EDA: Leveraging New Technologies for Product Verification

There is perhaps no sector more competitive than the modern electronics industry. Macro-trends, including artificial intelligence, 5G, and the internet of things (IoT), continue to propel dramatic growth. Read more…

TACC’s Upgraded Ranch Data Storage System Debuts New Features, Exabyte Potential

May 22, 2019

There's a joke attributed to comedian Steven Wright that goes, "You can't have everything. Where would you put it?" Users of advanced computing can likely relate to this. The exponential growth of data poses a steep challenge to efforts for its reliable storage. For over 12 years, the Ranch system at the Texas Advanced Computing Center... Read more…

By Jorge Salazar, TACC

Cray’s Emerging Market & Technology Director Arti Garg Peers Around HPC/AI Corner

May 23, 2019

In her position as emerging market and technology director at Cray, Arti Garg doesn't just have a front-row seat to the future of computing, she plays an active role in making that future happen. Key to Garg's role is understanding how deep learning scientists are using state-of-the-art HPC infrastructures and figuring out how to push those limits further. Read more…

By Tiffany Trader

Combining Machine Learning and Supercomputing to Ferret out Phishing Attacks

May 23, 2019

The relentless ingenuity that drives cyber hacking is a global engine that knows no rest. Anyone with a laptop and run-of-the-mill computer smarts can buy or re Read more…

By Doug Black

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

HPE to Acquire Cray for $1.3B

May 17, 2019

Venerable supercomputer pioneer Cray Inc. will be acquired by Hewlett Packard Enterprise for $1.3 billion under a definitive agreement announced this morning. T Read more…

By Doug Black & Tiffany Trader

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

CCC Offers Draft 20-Year AI Roadmap; Seeks Comments

May 14, 2019

Artificial Intelligence in all its guises has captured much of the conversation in HPC and general computing today. The White House, DARPA, IARPA, and Departmen Read more…

By John Russell

Cascade Lake Shows Up to 84 Percent Gen-on-Gen Advantage on STAC Benchmarking

May 13, 2019

The Securities Technology Analysis Center (STAC) issued a report Friday comparing the performance of Intel's Cascade Lake processors with previous-gen Skylake u Read more…

By Tiffany Trader

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

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

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

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

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

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

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

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

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

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

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

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

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