IEEE Circuits and Systems Magazine - Q2 2018 - 97

Bio-inspired models that mimic the way in which images are processed
at retina visual pathways can be implemented
by extending the CNNUM concept.
All the signal variables are continuous, except for the
discreteness in space (pixels). At the same time, visual
microprocessors retain the extraordinary strength of
digital computers, their unconstrained variability via
programming or software. Obviously, such software
and related algorithms are different from conventional ones.
Functional mechanisms underlying CNNUM processing capabilities are briefly summarized at the top-right
inset of Fig. 2. It shows that processing pixel cells include
circuits structures to control the dynamic evolution of a
dynamic state driven by:
■ two-dimensional gain transformations of the inputs (matrix B);
■ two-dimensional offset factors (parameter z); and
■ subjected to dynamic interactions among the states
of neighbour cells (matrix A).
Complex spatial/temporal tasks can be performed by
proper setting of the parameters. Also, programs can
run by executing algorithms in a process where parameter values are changed by software, thus covering
a very large variety of vision tasks [4].

Also, as Fig. 5 illustrates, bio-inspired models that mimic the way in which images are processed at retina visual
pathways can be implemented by extending the CNNUM
concept to include two state variables per cell. Complex
spatial-temporal dynamics can be generated in this way
to achieve computation through waves. The outcome of
such processing can be used to develop control feedback
actions to adapt the response of photo-receptors to local
image features. Besides simple resistive grid filtering,
it is possible to program other spatial-temporal processing operators into the model core, such as non-linear and
anisotropic diffusion, among others.
On CVIS Architectural Choices
Most efficient CVISs architectures employ mixed-signal
Multi-Functional sensory-processing PixelS (MFPS)
for fully-parallel completion of computational-intensive
early vision tasks, followed by sub-sampled topographic
processor arrays (typically digital), processors-per-column and scalar processors [33]. MFPSs actually make
the next evolutionary step of CMOS pixels, after passive pixels (PPS) and active pixels (APS), by embedding

Layer 1

Σ

x1,ij

1
τ1

b1u1,ij + z1

2
0
-2
0
10

f()

20

30 0

10

20

30

Photoreceptor Cells

y1,ij

g()

OPL
Horizontal Cells
Bipolar Cells

Layer 2
IPL
Σ

b2u2,ij + z2

x2,ij

1
τ2

g()

Amacring Cells
f()

y2,ij

2
0
-2
0
10

Ganglion Cells

20

30 0

10

20

30

Figure 5. coupled processing layers and waves generated by the cacE cnnum chip emulating the behavior of Inner and Outer
Plexiform Layers in mammal retinas [45].

sEcOnd quartEr 2018

IEEE cIrcuIts and systEms magazInE

97



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