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IBM Demos In-Memory Massively Parallel Computing
Source: Colin Johnson

Today’s experimental non-von Neumann computing architectures mostly make use of memristive devices modeled on the human brain; they do not separate data memory from computing hardware and thus avoid the inefficiency of von Neumann computers’ repeated load/store operations. Now IBM Research (Zurich) has demonstrated a way to mass-produce 3-D stacks of phase-change memory (PCM) to perform memristive calculations 200 times faster than von Neumann computers. The in-memory coprocessor uses algorithms that exploit the dynamic physics of phase-change memories simultaneously on myriad cells, similar to the way millions of neurons and trillions of synapses in the brain operate in parallel.

The development, which IBM will demonstrate in December at the International Electronic Devices Meeting (IEDM), could return the company to the brink of hardware dominance.

“We have demonstrated that computational primitives using non-von Neumann processors can be used to do machine learning tasks,” IBM Fellow Evangelos Eleftheriou told EE Times. “So far, we predict a speedup of 200 times for our non-von Neumann correlation detection algorithm compared to using state-of-the-art computing systems, but we have many other computational primitives on the way that we will demonstrate later this year.”

The new paradigm combines PCM crystallization dynamics with an acceleration methodology called in-memory computing, which loads all data into RAM instead of swapping data sets into and out of mass memory (hard drives or flash). IBM’s approach does not force the in-memory values through the von Neumann bottleneck of a central processing unit; rather, it leaves the initial-state memory values in each PCM cell and uses a specialized memory controller to perform simultaneous, parallel operations on the cells. Calculations are performed in place by harnessing the physical properties of the phase-change RAMs.

Diagram of IBM’s support circuitry (green and blue) for its PCM devices (brown), which perform simultaneous operations on cells (details, right).
Source: IBM


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