Today that insight has been revitalized by HRL Laboratories LLC
(formerly Hughes Research Laboratories) and its Center for Neural and
Emergent Systems (CNES) which has embraced the use of memristors as
artificial synapses with funding from DARPA's SyNAPSE program. In
conjunction with the University of Michigan, HRL recently outlined its
progress toward such cognitive computers in a paper is entitled A Functional Hybrid Memristor Crossbar-Array/CMOS-System, for Data Storage and Neuromorphic Applications.
Click on image to enlarge.
HRL's memristor crossbar array fabricated atop a CMOS chip
can store 10 Gbits per square centimeter.
the common titanium-dioxide material, HRL Labs has created a refined
material stack with intrinsic rectification properties that it claims
solve the "sneak path" problem that slowed early development efforts of
memristive materials in crossbar arrays.
"These memristors have
an intrinsic diode like behavior that prevents currents from sneaking
through in the reverse bias direction and affecting the stored memory
values," said program manager and principal investigator Narayan
HRL's formulation storea precise analog values
corresponding to the synaptic strength between brain-like neurons, then
put sthe neurons themselves on a companion chip.
neuromorphic architecture uses a rich programmable brain-like
connectivity," said Srinivasa. "To enable this connectivity, we have
decoupled the memristor array from the neuromorphic architecture,
putting the crossbar arrays on one chip to store synaptic conductances
which are then driven by a separate neuromorphic chip."
first iteration, HRL Labs two-chip solution emulates a single layer of a
real brain. However, the most interesting aspects of neural learning
use up to six layers. For instance, in the visual cortex each layer of
processing progressively associates different orientations from which an
object might be viewed. In other words, a single layer could learn to
recognize a two-dimensional silhouette of an object, but would fail to
identify that same object if it is turned sideways. However, after
passing through all six layers that same object can be recognized
regardless of from which direction it is being viewed. But to emulate
all six layers of the human cortex, a 3-D memristive array with six
layers will needed.
"At present we are focused on building 2-D
crossbars--to minimize risk and maximize our probability of success,"
said Srinivasa. "But eventually we want to scale the arrays in the third
dimension to emulated the true synapse geometries found in the brain.