Could analog computing accelerate complex computer simulations?

March 19, 2015

DARPA is looking for new processing paradigms that have the potential to overcome current barriers in computing performance. “Old fashioned” analog approaches may be part of the solution. (credit: DARPA)

DARPA announced today, March 19, a Request for Information (RFI) on methods for using analog approaches to speed up computation of the complex mathematics that characterize scientific computing.

“The standard [digital] computer cluster equipped with multiple central processing units (CPUs), each programmed to tackle a particular piece of a problem, is just not designed to solve the kinds of equations at the core of large-scale simulations, such as those describing complex fluid dynamics and plasmas,” said Vincent Tang, program manager in DARPA’s Defense Sciences Office.

These critical equations, known as partial differential equations, describe fundamental physical principles like motion, diffusion, and equilibrium, he notes. But they involve continuous rates of change over a large range of physical parameters relating to the problems of interest, so they don’t lend themselves to being broken up and solved in discrete pieces by individual CPUs. Examples of such problems include predicting the spread of an epidemic, understanding the potential impacts of climate change, or modeling the acoustical signature of a newly designed ship hull.

What if there were a processor specially designed for such equations? What might it look like?

The differential analyzer at the Moore School of Electrical Engineering, University of Pennsylvania, c. 1942–1945, using a wheel-and-disc mechanism. How might a nanotech version work? (credit: U.S Government/public domain)

Analog computers solve equations by manipulating continuously changing values instead of discrete digital measurements, and have been around for more than a century. In the 1930s, for example, Vannevar Bush—who a decade later would help initiate and administer the Manhattan Project—created an analog “differential analyzer” that computed complex integrations through the use of a novel wheel-and-disc mechanism.

Their potential to excel at dynamical problems too challenging for today’s digital processors may today be bolstered by other recent breakthroughs, including advances in microelectromechanical systems, optical engineering, microfluidics, metamaterials and even approaches to using DNA as a computational platform.

So it’s conceivable, Tang said, that novel computational substrates could exceed the performance of modern CPUs for certain specialized problems, if they can be scaled and integrated into modern computer architectures.


DARPA’s RFI is called Analog and Continuous-variable Co-processors for Efficient Scientific Simulation (ACCESS), available here: The RFI seeks new processing paradigms that have the potential to overcome current barriers in computing performance. “In general, we’re interested in information on all approaches, analog, digital, or hybrid ones, that have the potential to revolutionize how we perform scientific simulations,” Tang said.

The RFI invites short responses that address the following needs, either singly or in combination:

  • Scalable, controllable, and measurable processes that can be physically instantiated in co-processors for acceleration of computational tasks frequently encountered in scientific simulation
  • Algorithms that use analog, non-linear, non-serial, or continuous-variable computational primitives to reduce the time, space, and communicative complexity relative to von Neumann/CPU/GPU processing architectures
  • System architectures, schedulers, hybrid and specialized integrated circuits, compute languages, programming models, controller designs, and other elements for efficient problem decomposition, memory access, and task allocation across multi-hybrid co-processors
  • Methods for modeling and simulation via direct physical analogy

Technology development beyond these areas will be considered so long as it supports the RFI’s goals. DARPA is particularly interested in engaging nontraditional contributors to help develop leap-ahead technologies in the focus areas above, as well as other technologies that could potentially improve the computational tractability of complex nonlinear systems.