Synthetic Neural Circuits Using Current-Domain Signal Representations G .

Synthetic Neural Circuits Using Current-Domain Signal Representations G .
Communicated by John Wyatt
Synthetic Neural Circuits Using Current-Domain
Signal Representations
Andreas G. Andreou
Kwabena A. Bpahen
Electrical and Computer Engineering, The Johns Hopkins University,
Baltimore, MD 21218 USA
We present a new approach to the engineering of collective analog
computing systems that emphasizes the role of currents as an appropriate signal representation and the need for low-power dissipation
and simplicity in the basic functional circuits. The design methodology and implementation style that we describe are inspired by the
functional and organizational principles of neuronal circuits in living
systems. We have implemented synthetic neurons and synapses in
analog CMOS VLSI that are suitable for building associative memories and self-organizing feature maps.
1 Introduction
Connectionist architectures, neural networks, and cellular automata (Rumelhart and McClelland 1986; Kohonen 1987; Grossberg 1988; Toffoli 1988)
have large numbers of simple and highly connected processing elements
and employ massively parallel computing paradigms, features inspired
by those found in the nervous system. In a hardware implementation,
the physical laws that govern the cooperative behavior of these elements
are exploited to process information. This is true both at the system
level, where global properties such as energy are used, and at the circuit
level, where the device physics are exploited. For example, Hopfield’s
network (1982) uses the stable states of a dynamic system to represent
information; associative recall occurs as the system converges to its local
energy minima. On the other hand, Mead’s retina (1989) uses the native
properties of silicon transistors to perform local automatic gain control.
In this paper we discuss the importance of signal representations
in the implementation of such systems, emphasizing the role of currents. The paper is organized into six sections: Section 2 describes the
roles played by current as well as voltage signals. The metal-oxidesemiconductor (MOS) transistor, the basic element of complementaryMOS (CMOS) very large scale integration (VLSI) technology, is introduced in Section 3. In the subthreshold region, the MQS transistor’s
behavior strongly resembles that of the ionic channels in excitable cell
Neural Computation 1, 489-501 (1989) © 1989 Massachusetts Institute of Technology
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Andreas G. Andreou and Kwabena A. Boahen
membranes. Translinear circuits, a computationally rich class of circuits
with current inputs and outputs, are reviewed in Section 4. These circuits
are based on the exponential transfer characteristics of the transistors, a
property that also holds true for certain ionic channels. Simple and useful circuits for neurons and synapses are described in Section 5. Proper
choice of signal representations leads to very efficient realizations; a single line provides two-way communication between neurons. Finally, a
brief disscussion of the philosophy behind the adopted design methodology and implementation style is presented in Section 6.
2 Signals
In an electronic circuit, signals are represented by either voltages or
currents.1 A digital CMOS circuit depends on two well defined voltage levels for reliable computation. Currents play only an incidental role
of establishing the desired voltage levels (through charging or discharging capacitive nodes). Since the abstract Turing model of computation
does not specify the actual circuit implementation, two distinct current
levels will work as well. In contrast, the circuits described here use analog
signals and rely heavily on currents; both currents and voltages having
continuous values.
At the circuit level, Kirchoff’s current law (KCL) and Kirchoff’s voltage law (KVL) are exploited to implement computational primitives.
KCL states that the sum of the currents entering a node equals the sum
of the currents leaving it (conservation of ,charge). So current signals
may be summed simply by bringing them to the same node. KVL states
that the sum of voltages around a closed loop is zero (conservation of
energy). Therefore, voltage signals may be summed as well. Actually,
the translinear circuits described in Section 4 rely on KVL while avoiding
the use of differential voltage signals (not referenced to ground).
Voltages are used for communicating results to different parts of the
system or for storing information locally. Accumulation of charge on a
capacitor (driven by a current source) results in a voltage that represents
local memory in the system. This also implements the useful function
of temporal integration. Distributed memory can be realized using spatiotemporal patterns of charge, following the biological model (Freeman et
al. 1988; Eisenberg et al. 1989). In this type of memory, stored information
is represented by limit-cycles in the phase space of a dynamic system.
However, in current VLSI implementations, memory is represented as
point attractors (i.e., a stable equilibrium) in the spatial distributions of
charge, as, for example, in our bidirectional associative memory chips
(Boahen et al. 1989a,b).
1
This may be an area in which biological systems have a distinct advantage by
employing both chemical and electrical signals in the computation.
Synthetic Neural Circuits Using Current-Domain Signal Representations
491
+m-
Figure 1: The MOS transistor. (a) Structure.
3 Devices
The MOS transistor, shown in Figure la, has four terminals: the gate (G),
the source (S), the drain (D), and the substrate (B, for bulk). The gate and
source potentials control the charge density in the channel between the
source and the drain, and hence the current passed by the device. The
MOS transistor is analogous to an ensemble of ionic channels in the lipid
membrane of a cell controlled by the transmembrane potential.
We operate the MOS transistor in the so-called "off" region, characterized by gate source voltages that are below the threshold voltage.
In this region charge transport is by diffusion from areas of high carrier
concentration to energetically preferred areas of lower carrier concentration. This is referred to as weak-inversion (ViHoz and Fellrath 1977) or
subthreshold conduction (Mead 1989; Maher et al. 1989). The transfer characteristics are shown in Figure lb. These curves are very similar to those
for the calcium-controlled sodium channel, Hille (1984, p. 317). In both
cases the exponential relationships arise from the Boltzmann distribution.
The subthreshold current is given by2
= I0e[(1-K)Vbs]/VTeKVgs/VT (1 - e-Vds/VT + Vds/V0)
2
(3.1)
For the sake of brevity, we discuss only the n-type device whose operation depends
on the transport of negative charges. The operation of a p-type device is analogous.
492
Andreas G. Andreou and Kwabena A. Boahen
0.7
. . .
.
.
0.6
0.5
.
0.4
o .2
l V d s (V)
0.4
0.6
0.8
1.0
Figure 1: Cont’d (b) transfer characteristics, (C) output characteristics. To first
order, the current is exponentially dependent on both the substrate and the gate
voltages. In (b) the dots show measured data from an n-type transistor of size
4 x 4 µm, with V d s = 1.0 V . The solid lines are obtained using equation 3.1 with
I0 = 0.72 x 10-18A and K = 0.75. The data in (b) are for a similar device with
V bs = o; it is fitted with V0 = 15.0 V .
where I0 is the zero-bias current and K measures the effectiveness of
the gate potential in controlling the channel current. To first order, the
effectiveness of the substrate potential is given by (1- K ) ; VT = k T / q , the
thermal voltage, equals 26 mV at room temperature, and V0 is the Early
voltage, which can be determined from the slope of the I d s versus Vds
curves. Notice that I d s changes by a factor of e for a V T /K = 33.0 mV
change in V gs . This drain current equation is equivalent to that in Maher
Synthetic Neural Circuits Using Current-Domain Signal Representations
493
et al. (1989); however, in this form the dependence on the substrate voltage is explicit. This three parameter model is adequate for rough design
calculations but not for accurate simulation of device operation. Refer
to Mead (1989, Appendix B) for a more elaborate model. Subthreshold
currents are comparable to currents in cell membranes; they range from
a few picoamps to a few microamps.
For a given gate-source voltage V gs , the MOS transistor has two distinct modes of operation, determined by the drain-source voltage V d s , as
shown by the output characteristicsin Figure 1c. The behavior is roughly
linear if V d s is less than Vdsat
100 mV; small changes in V d s cause proportional changes in the drain current. For voltages above V d s a t , the
current saturates. In this region the MOS transistor is a current source
with output conductance:
The change in drain current for a small change in gate voltage is given
by
g m is called the transconductance because it relates a current between two
nodes to a voltage at a third node. As we shall see, the subthreshold
MOS transistor is a very versatile circuit element because gm >> g d s a t .
4 Circuits
Area-efficient (compact) functional blocks can be obtained by using the
MOS transistor itself to perform as many circuit functions as possible.
The three possible circuit configurations for the transistor are shown in
Figure 2:
In the common-source mode, it is an inverting amplifier with high
voltage gain: g m / g d s a t .
In the common-drain mode, it is a voltage follower with low output
resistance; 1/gm.
In the common-gate mode, it is a current buffer with low output
conductance; g d s a t .
In the synthetic neuronal circuits described in the next section the
inverting amplifier is used as a feedback element to obtain more ideal
circuit operation while the voltage follower and the current buffer are
used to effectively transfer signals between different circuits.
494
Andreas G. Andreou and Kwabena A. Boahen
The actual computations are performed by current-domain (or currentmode) circuits. A Current-Domain (CD) circuit is one whose input signals
and output signals are currents. The simplest CD circuit is shown in Figure 3. This circuit copies the input current to the output and reverses
its direction. It is appropriately named a current mirror. The circuit has
just two transistors: an input transistor and an output transistor. The
input current I in is converted to a voltage Vb by the input transistor. This
voltage sets the gate voltage of the output transistor. Thus, both devices
have the same gate-source voltages and will pass the same current if
they are identical and have the same drain and substrate voltages.
In practice, device mismatch produces random variations in the output current, while the nonzero drain conductance results in systematic
variations. More complicated mirror circuits, for example, the Wilson
mirror or the Complex mirror (Pavasovic et al. 1988), may be used to
obtain lower output conductance. By using more output devices, several
copies of the input current can be obtained. The current mirror is analogous to a basic synapse structure in biological systems: it is simple in
(a) Common-source,
Figure 2: MOS transistor circuit configurations.
(b) common-drain, and (C) common-gate modes of operation. In (a) voltage
gain is obtained by converting the current produced by the device's transconductance to a voltage across its drain conductance. In (b) a voltage follower/buffer is realized; the gate-source drop is kept constant by using a fixed
bias current and setting Vbs = O. In (C) the device serves as a current buffer
by transferring the signal from its high conductance source terminal to the low
conductance drain node.
Synthetic Neural Circuits Using Current-Domain Signal Representations 495
Y
Figure 3: Current mirror circuits using (a) n-type and (b) ptype transistors.
These circuits provide an output current that equals the input current if the devices are perfectly matched. For subthreshold operation, we observe variations
of about l0%, on average: using 4 x 4pm devices.
form, it enforces unidirectional information flow, and it can function over
a large range of input and output signal levels.
Translinear circuits (Gilbert 1975)are a computationally powerful subclass of CD circuìts. A translinear circuit is defined as one whose Operation depends on the linear relationship between the transconductance
and the channel current of the active devices (Equation 3.3).3 The current
mirror in subthreshold operation is an example of a translinear circuit.
The Translinear Principle (Gilbert 1975) can be used to synthesize a wide
variety of circuits to perform both linear and nonlinear operations on
the current inputs, including products, quotients, and power terms with
fixed exponents. The Gilbert current multiplier is one of the better known
translinear circuits. Gilbert's elegant analog array normalizer (1984) is an
example of a more powerful translinear circuit. One fascinating aspect
of translinear circuits is that although the currents in its constitutive elements (the transistors) are exponentially dependent on temperature, the
overall input/output relationship is insensitive to isothermal temperature
variations. The effect of small local variations in fabrication parameters
can also be shown to be temperature independent. Finally, translinear circuits are simple, because an analog representation is used and the native
device properties provide the computational primitives.
3
Translinear circuits have traditionally been built using bipolar transistors.
Andreas G. Andreou and Kwabena A. Boahen
496
5 Synapses and Neurons
In a neuronal circuit, the interaction between neurons is mediated by a
large variety of synapses (Shepherd 1979). A neuron receives its inputs
from other neurons through synaptic junctions that may have different
efficacies. In a VLSI system, the synapses are implemented as a twodimensional array with the neurons on the periphery. This is because
O(N2) synapses are required in a network with N neurons. Generally,
two sets of lines (buses) are run between the neurons and the synaptic
array; one carries neuronal output to the synapses and the other feeds
input to the neurons. However, in networks with reciprocal connections,
such as the bidirectional associative memory (Boahen et al. 1989a,b),
proper choice of signal representations leads to a more efficient implementation.
Our circuit implementations for neurons and synapses are shown
in Figure 4. These circuits use voltage to represent a neuron’s output
(presynapticsignal) and cùrrent to represent its inputs (postsynapticSignals). Since currents and voltages may be independently transmitted along
the same line, these signal representations allow a neuron’s output and
I
n2
I
Iout
M1
1
Figure 4: Circuits for synapsesand neurons. (a)Reciprocal synapse and (b)neuron. These circuits demonstrate efficient signal representations that use a single
line to provide two-way communication. A voltage is used to represent information going one way while a current is used to send information the other
way. The synapse circuit in (a) provides bidirectional interaction between two
neurons connected to nodes n1 and n2. The neuron circuit in (b) sends out a
voltage that mirrors its output current Iout in the synapses while receiving the
total current I a from these synapses.
Synthetic Neural Circuits Using Current-Domain Signal Representations 497
inputs to be communicated using just one line. Voltage output facilitates
fan-out while current input provides summation. Thus, in close analogy
to actual neuronal microcircuits, the output signal is generated at the
same node at which inputs are integrated.
The two transistor synapse circuit (Figure 4a) provides bidirectional
interaction between neurons connected to nodes n1 and n2; each transistor
serves as a synaptic junction. When S is at ground, voltages applied at
nodes n1 and n2 are transformed into currents by the transconductances
of M2 and M1, respectively. If these voltages exceed Vdsat, the transistors
are in saturation and act as current sources. Thus, changes in the voltage
at n1(n2) do not affect the current in M1(M2). Actually, for a small change
in Vn1,the changes in I1 and I2 arerelated by
This gives
Hence, we can double I2 (using the voltage at n1) while disturbing I1 by
only 0.2%.The interaction is turned off by setting S to a high voltage, or
modulated by applying an analog signal to the substrate.
The circuit for the neuron also uses just two transistors (Figure 4b).
The net input current I a (for activation), formed by summing the inputs
at node n, is available at the drain of M1. This device buffers the input
current and controls the output voltage. I a is fed through a nonlinearity,
for example, thresholding (not shown), to obtain Iout, which sets the output voltage Vout. This is accomplished by using M1 as a voltage follower
and providing feedback through M2, which functions as an inverting amplifier; M1 adjusts Vout so that the current in M2 equals Iout. Hence, Vout
will mirror I out in the synapses. The feedback makes the output voltage
insensitive to changes in the input current, Ia. Actually, the output conductance is approximately gm1gm2/gdsat2; it is increased by a factor equal
to the gain provided by M2.
In this case, a small change in Vout produces changes in I a and I s (the
postsynaptic copy of I o u t ) given by
Hence, if I, doubles, the resulting change in Vout decreases I , by only
0.2%-just as in the previous case. Note that Iout must always exceed
a few picoamps to keep Vout above Vdsat. The characteristics of these
498
Andreas G. Andreou and Kwabena A. Boahen
Figure 5: Characteristics of a synthetic neuronal circuit. (a) A simple circuit
consisting of two neurons (nl and n 2 ) and a synapse ( S ) was built and tested to
demonstrate the proposed communication scheme. The currents sent by n l (n 2 )
and that received by n 2 (n l ) are denoted by I12(I21) and Î12(Î21), respectively.
Continued on next page.
circuits, designed using 4 p m x 4 p m devices and fabricated through MOSIS, are shown in Figure 5a-c.
6 Discussion
The adopted design methodology is governed by three simple principles:
First, the computation is carried out in the analog domain; this gives
simple functional blocks and makes efficient use of interconnect lines.
Second, the physical properties of silicon-based devices and circuits are
used synergetically to obtain the desired result. Third, circuits are designed with power dissipation and area efficiency as prime engineering
constraints, not accuracy or speed. We believe power dissipation will be
a serious limitation in large scale-analog computing hardware. Unlike
digital integrated circuits, the massive parallelism and concurrency attainable with analog computation impose serious limits on the amount
of power that each circuit can dissipate. This is why we operate the
devices with currents in the nanoamps range and, if possible, picoamps,
about the same current levels found in biological systems.
This approach is similar to, and strongly influenced by, that of Mead’s
group at Caltech. Our approach is more minimalistic, we view the transistor itself as the basic building block; not the transconductance amplifier. Thus, currents, rather than differential voltages, are the primary
signal representation.
Synthetic Neural Circuits Using Current-Domain Signal Representations
I21^ (nA)
1o o--
499
V b s (mV)
8 O--
60--
O
4o--
- 50
2 o--
-100
20
40
60
80
l I12 (nA)
100
(b)
I12^
100
Vbs (mV)
6 O--
4o--
20
40
60
80
l I12 (nA)
100
Figure 5: Cont'd Plots (b) and (c) show how I^12 and I^ 21 vary as I 1 2 is stepped
from 2.0nA to 100nA while I21 is held at 50nA, for various substrate bias voltages. The values Vbs = O, -50, and -100mV correspond to weights of 0.93, 0.57,
and 0.33, respectively. Notice that these weights modulate signals going both
ways symmetrically.
We are not concerned about accuracy or matching in the basic elements because biological systems perform well despite the limited precision of their neurons and synaptic connections. The emerging view is that
this is a result of the collective nature of the computation performedwhereby large numbers of elements contribute to the final result. From
500
Andreas G. Andreou and Kwabena A. Boahen
a system designer’s point of view, this means that random variations in
transistor characteristics are not deleterious to the system’s performance,
whereas systematic variations are and must therefore be kept to a minimum. Indeed, we have observed this in silicon chips.
The translinear property of the subthreshold MOS transistor provides
a very powerful computational primitive., This property arises from the
highly nonlinear relationship between the gate potential and the channel
current. In fact, the exponential is the strongest nonlinearity relating a
voltage and a current in solid-state devices (Shockley 1963; Gunn 1968).
It is interestingto note that the same property holds for voltage-activated
ionic channels, however, the conductance dependence is steeper due to
correlated charge control of the current (Hille 1984, p. 55). In translinear
(current-domain) circuits we have seen a classical example of how a rich
form for circuit design emerges from the properties of the basic units
(MOS transistor in subthreshold).
To summarize, we have addressed some issues related to the engineering of collective analog computing systems. In particular, we have
demonstrated that currents are an appropriate analog signal representation. Current levels comparable to those in excitable membranes are
achieved by operating the devices in the subthreshold region resulting in
manageable power dissipation levels. This design methodology and implementation style have been used to build associative memories (Boahen
et al. 1989a, b) and self-organizing feature maps in analog VLSI.
Acknowledgments
This research was funded by the Independent Research and Development
program of the Applied Physics Laboratory; we thank Robert Jenkins
for his personal interest and support. The authors would like to thank
Professor Carver Mead of Caltech for encouraging this work. Philippe
Pouliquen and Marc Cohen made excellent comments on the paper and
Sasa Pavasovic helped with acquiring the experimental data. We are
indebted to Terry Sejnowski, who provided a discussion forum and important insights in the field of neural computation at Johns Hopkins University. We thank the action editor, Professor John Wyatt, for his critical
review and insightful comments.
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