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Transcript of Advanced Technologies for Brain-Inspired Computing
7/26/2019 Advanced Technologies for Brain-Inspired Computing
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Advanced Technologies for Brain-Inspired Computing
Abstract – This paper aims at presenting how new technologies
can overcome classical implementation issues of Neural
Networks. Resistive memories such as Phase Change Memories
and Conductive-Bridge RAM can be used for obtaining
low-area synapses thanks to programmable resistance also
called Memristors. Similarly, the high capacitance of Through
Silicon Vias can be used to greatly improve analog neurons and
reduce their area. The very same devices can also be used for
improving connectivity of Neural Networks as demonstrated by
an application. Finally, some perspectives are given on the
usage of 3D monolithic integration for better exploiting the
third dimension and thus obtaining systems closer to the brain.
I. IntroductionBrain-inspired computing has long been a nice theoretical
research topic facing implementation issues. Even with
powerful computing capabilities on some of the hardest
problems, very few industrial applications have appeared.
As largely known, implementation limitations are linkedto two main elements:
- Difficulty to “emulate” the behavior of synapses and
neurons with transistors. Classical CMOS technology
is not aimed at providing devices compatible with
neurons requirements.
- Difficulty to connect neurons. This is linked to
2D-layered implementation used for simplifying
CMOS integration.
Some solutions have been proposed to cope with these
issues. Analog neuron is an elegant implementation solution
to reduce the number of transistors needed for a neuron.
However, while reducing the number of transistors, large
capacitances are needed for obtaining a good RC delay.On the connection purpose, time multiplexing is currently
used, but it limits the overall performance while increasing
the power consumption due to high frequency links.
Moreover, the increasing capacitance of wires in advanced
technologies limits this solution.
A global conclusion of all these works is the relative
inexpediency of standard CMOS with Neural Networks
(NN) implementation.
In recent years, new technologies have appeared that can
change the game. Some of these technologies and their
application or potential of application to NN are presented in
this paper.
In section II, resistive memories are used to improveimplementation of synapse thanks to their nice properties.
Both Phase Change Memories (PCM) and
Conductive-Bridge Random Access Memories (CBRAM)
are studied for this purpose.
In section III, analog neurons are revisited through the
usage of high capacitances of Through Silicon Vias (TSV)used for 3D stacking. TSV also opens the way towards 3D
integration and interconnection length reduction which is
discussed in section IV.
Finally, emerging 3D monolithic technology is discussed
in section V. This technology allows high vertical
interconnects density, thus becoming closer to the brain and
opening the way to a high range of possibilities to NN
implementation.
II. Exploiting Resistive MemoriesMemristive memories can be defined as two-terminal
devices whose conductance can be modulated by flowing
current though it. In this section, we show how memristorcan be used to produce brain-like circuits and in particular
arrays of artificial synapses. The functional analogy between
memristive technologies and the behavior of a synapse has
been anticipated by Chua [1] and later popularized by
Strukov [2].
Since then several authors have shown that it was a
fruitful idea. Most notably, Snider [3] showed the concept of
using memristive memories to implement the Spike Timing
Dependent Plasticity (STDP) learning rule, by virtue of their
own physics. This idea has since been experimentally
demonstrated to be actually working by several groups:
University of Michigan using Si-Ag devices [4], University
of Aachen with nano-ionics devices [5] and Alibart et al.with Organic transistors (NOMFET) [6].
However, there is a bit more than the basic functionality
of a single device to yield an actual working memristive
based brain-like circuit. As a matter of fact, the circuit
architecture allowing the exploitation of such devices is of
utmost importance. Depending on the polarity and electrical
characteristics of investigated devices, three types of circuits
have been identified which are now described in the
following paragraphs.
Fabien Clermidy , Rodolphe Heliot , Alexandre Valentian , Christian Gamrat ,Olivier Bichler
**, Marc Duranton
**, Bilel Blehadj
# and Olivier Temam
#
*CEA-LETI, Grenoble, FRANCE, [email protected]
**CEA-LIST, Paris, FRANCE, [email protected]#INRIA, Paris, FRANCE, [email protected]
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A.
Circuits for Bipolar Memristors
Most of the works on memristive devices that have been
published over the last couple of years focus on bipolar
resistive switching devices [2] [3] [4] [5]. Indeed, these
devices exhibit characteristics that are the closest to the
original Memristor predicted by Chua. Their resistance can be gradually increased or decreased with opposite polarity
voltage pulses and the resistance change is cumulative with
the previous state of the device, which make them
particularly suitable to implement synaptic-like
functionality.
Several (simplified) models have been proposed to
develop nano-architectures capable of learning with
memristive devices. In [7], a new behavioral model
especially tailored at modeling the conductance evolution of
some popular memristive devices in pulse regime for
synaptic-like applications is introduced. This model
demonstrates high tolerance of NN architectures to
Memristor variability.A biologically-inspired spiking NN-based computing
paradigm which exploits the specific physic of those devices
is presented in [8]. In this approach, CMOS input and output
neurons are connected by bipolar memristive devices used as
synapses. It is natural to lay out the nanodevices in the
widely studied crossbar as illustrated on Figure 1, where
CMOS silicon neurons and their associated synaptic driving
circuitry are the dots, the squares being the nanodevices.
Learning is competitive thanks to lateral inhibition and fully
unsupervised using a simplified form of STDP.
Using this topology, comparable performance to
traditional supervised networks has been measured [8] on
the textbook case of character recognition, despite extremevariations of various memristive devices’ parameters. With
the same approach, unsupervised learning of temporally
correlated patterns from a spiking silicon retina has also
been demonstrated. When tested with real-life data, the
system is able to extract complex and overlapping
temporally correlated features such as car trajectories on a
freeway [9].
Figure 1: Basic circuit topology. Wires originate from CMOS input
layer (horizontal black wires) and from the CMOS output layer
(vertical gray wires). Memristive nanodevices are at the
intersection of the horizontal and vertical wires [8].
B. Circuits for Unipolar Memristors (PCM)
Among the resistive technologies, Phase-Change Memory
(PCM) has good maturity, scaling capability, high endurance,
and good reliability [10]. PCM resistance can be modified
by applying a temperature gradient modifying the material
organization between an amorphous and a crystalline phase.
The amorphous region inside the phase change layer can be
crystallized by applying Set pulses, thus increasing device
conductance. It was shown that the magnitude of the relative
increase in conductance can be controlled by the pulseamplitude and by the equivalent pulse width [11].
Amorphization, on the other hand is a more power-hungry
process and is not progressive with identical pulses. The
current required for amorphization is typically 5–10 times
higher than for crystallization, even for state-of-the art
devices.
To overcome these issues, a novel low-power architecture
“2-PCM Synapse” was introduced in [12]. The idea is to
emulate synaptic functions in large scale neural networks
thanks to two PCM devices constituting one synapse as
shown in Figure 2. The two devices have an opposite
contribution to the neuron’s integration. When the synapse
needs to be potentiated, the Long Term Potentiation (LTP)PCM device undergoes a partial crystallization, increasing
the equivalent weight of the synapse. Similarly, when the
synapse must be depressed, the Long Term Depression
(LTD) PCM device is crystallized. As the LTD device has a
negative contribution to the neuron’s integration, the
equivalent weight of the synapse is reduced. Furthermore,
because gradual crystallization is achieved with successive
identical voltage pulses, the pulse generation is greatly
simplified.
Figure 2: (Left) Experimental LTP characteristics of Ge2Sb2Te5
(GST) PCM devices. For each curve, first, a reset pulse (7 V, 100
ns) is applied followed by 30 consecutive identical potentiating
pulses (2 V). Dotted lines correspond to the behavioral model fit
used in our simulations. (Right) 2-PCM synapse principle.
C. Using Bipolar Memories (CBRAM)
1T-1R CBRAM multi-level programming was also
proposed to emulate biological synaptic-plasticity. Like
PCM, it is difficult to emulate a gradual synaptic depression
effect using CBRAM, due to the abrupt nature of the
set-to-reset transition in those devices. Moreover, LTP
behavior is possible, but requires to gradually increasing the
select transistor gate voltage. This implies keeping a history
of the previous state of the synaptic device, thus leading to
additional overhead in the programming circuitry.
In [13], it is shown that when weak SET programming
conditions are used immediately after a RESET, a
probabilistic switching of the device appears, as illustrated
Equivalent
2-PCM synapse
I = ILTP - ILTD
ILTD
ILTP
From spiking pre-synaptic
neurons (inputs)
VRD
Spiking post-
synaptic neuron
(output)
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in Figure 3. Then, the switching probability can be tuned by
using the right combination of programming conditions.
This opens the way to exploit the intrinsic stochasticity of
CBRAM to implement synapses. Similarly to the system
level, a functional equivalence [14] exists between
multi-level deterministic synapses and binary probabilisticsynapses. Using this property, a low-power stochastic
neuromorphic system for auditory (cochlea) and visual
(retina) cognitive processing applications was proposed in
[13].
Figure 3: (Left) TEM of the CBRAM resistive element. (Right)
Stochastic switching of 1T-1R device during 1000 cycles using 100
ns pulse shows switching probability of around 0.5.
D. Conclusion
This section has demonstrated how synaptic weight
adaptation rules: LTP, LTD can be implemented using
various memristive devices. Thanks to this property, such
devices can lead to intrinsic implementation of STDP based
learning rule when coupled with a spike coding scheme [15].
It is safe to say that memristive technology is a promising
way for implementing synaptic arrays for brain-inspiredcomputing while neurons circuits will be implemented using
advanced CMOS technology as described in the following
section. This hybrid CMOS-memristive approach is
particularly appropriate for embedded neuromorphic
devices.
III. Analog neurons and 3D-TSVBeyond pure technological advantages, a mix between
advanced design and new technologies is an appealing way
to succeed in integrating more neurons and greatly increase
the overall performance of NN. In this section, we discuss
the advantages of analog neurons design style and their
combination with Through-Silicon-Vias (TSV) used in 3D
stacking technologies.Analog neurons are considered powerful computational
operators thanks to 1) compact design, 2) low power
consumption, 3) ability to interface sensors directly with the
processing part, and 4) computational efficiency. Even if
some analog neuron designs have already been proposed,
most are geared towards fast emulation of biological neural
networks [16] [17]. Consequently, these hardware neurons
contain a feedback loop for learning purposes [19] which is
irrelevant when learning can be done off-line such as many
current applications. Still, some of these designs have been
applied to computing applications [18], but they come with
the aforementioned overhead of their bio-inspired
motivation. Recently, we implemented and fabricated a
Leaky Integrate and Fire (LIF) analog neuron in 65nm
technology node (Figure 4) without feedback loop. This
neuron is capable of harnessing input signals ranging from
low (1kHz) to medium (1MHz) frequency, compatible with
most of the signal processing applications [20]. Note that the
aforementioned frequencies (1kHz to 1MHz) correspond tothe rate at which the neuron and I/O circuits can process
spikes per second, not the overall update frequency of the
input data. This frequency can potentially scale up to much
higher values by leveraging the massive parallelism of the
architecture: an “x” MHz input signal can be de-multiplexed
and processed in parallel using x input circuits.
Figure 4: Circuit diagram of the analog neuron (injection in purple,
leak in blue, reset in yellow, capacitance in orange, comparator in
red).
For instance, the motion estimation application processing
a black and white SVGA (800x600) image at 100Hz
corresponds to a maximum spike frequency of 48MHz
(800*600*100). This application requires 4*N p + 3 neurons
for N p pixels. Thus, (4*800*600+3)*100 spikes must be processed per second. Since the maximum processing rate of
a neuron is 1Mspikes/s in our design, we need
((4*800*600+3)*100)/106 = 193 neurons to process images
at a speed compatible with the input rate.
However, an analog neuron has both assets and
drawbacks. The main drawback is the need for a large
capacitance. The capacitance has a central role as it
accumulates spikes, i.e., it performs the addition of inputs. In
spite of this drawback, the analog neuron can remain a very
low-cost device: our analog neuron has an area of 120!m2 at
65nm. This area accounts for the capacitance size and the 34
transistors. Since the capacitance is implemented using only
two metal layers, most of the transistor logic can fitunderneath. As a result, most of the area actually
corresponds to the capacitance.
The analog neuron of Figure 4 operates as follows. When
a spike arrives at the input of a neuron, before the synapse, it
triggers the S"inc switch (see bottom left), which is bringing
the current to the capacitance via V I. The different
transistors on the path from S"inc to VI form a current mirror,
which aims at stabilizing the current before injecting it. Now,
if the synapse has value V, this spike will be converted into
a train of V pulses. These pulses are emulated by switching
on/off S pulse V times, inducing V current injections in the
capacitance. We have physically measured the neuron
energy at the very low value of 1.4pJ per spike (we used themaximum value of V = 127 for the measurement). The
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group of transistors on the center left part of the figure
implements the variable leakage of the capacitance (which
occurs when Sleak is closed) necessary to tolerate signal
frequencies ranging from 1kHz to 1MHz. After the pulses
have been injected, the neuron potential (corresponding to
the capacitance charge) is compared against the threshold byclosing Scmp, and an output spike is generated on Sspkout if the
potential is higher than the threshold.
As explained before, analog spiking neurons require large
capacitances to store the internal membrane voltage of the
neuron. Typical capacitances values are in the order of 0.5#1
pF, which corresponds to 50#200 µm$ capacitors in 32#65
nm technologies, and up to 50% of the total neuron area.
3D stacking is a technique that can provide massive
parallelism between layers by stacking them and directly
connecting them via a large number of Through-Silicon Vias
(TSVs). TSVs are used to create vertical interconnections in
3-D stacked chips. As shown in Figure 5, they are composed
of a metal wire isolated from the substrate. They are not perfect wires however, and act as MOS capacitors.
In traditional digital circuits, these TSVs are a significant
limitation of 3D stacking: they consume on-chip area and
suffer from their parasitic capacitance behavior [21].
However, we can actually take advantage of these
capacitances to implement the capacitors of spiking neurons,
turning a weakness into a useful feature.
An RLC %-shaped electrical model of TSVs was used, as
described in [21] (Figure 5, middle). The TSV model
features a resistance R tsv, and an inductance Ltsv. It is
isolated from the substrate by three capacitors Cox, Cdep and
Csi, while parasitic silicon substrate losses are represented bya conductance Gsi. All of these values are process dependent
and can change with TSV density, height, diameter,
operating frequency and oxide thickness.
Figure 5: Through Silicon Vias structure and electrical model
We designed an analog LIF neuron using the above TSV
model and compared it through Spice simulations against the
previously designed analog neuron. Figure 6 shows the
behavior of the neuron internal potential Vm for standard
(top) and TSV (bottom) neurons (for different densities).
When Vm reaches a threshold voltage Vth, Vm is reset to a
resting potential. With a medium density TSV (400/mm2), it
can be seen that the TSV-neuron exhibits the exact same
behavior as the standard neuron. TSVs with other densities
exhibit similar behavior, albeit with different time constants
[22].
Figure 6: Membrane potential of standard- and TSV-based (several
TSV densities) neurons.
3D stacking neuromorphic architectures with TSVs offers
massive tremendous opportunities. Figure 7 summarizes
some of the options that are offered. These different setups
come with different gains in terms of area or with additional
connectivity. Some of these applications are discussed in
next section.
Figure 7. Illustrations of (a) a standard 2D neuromorphic
architecture (b) a 3D-IC with standard 2D neurons, and (c) (d) a
3D-IC with TSV-based neurons
IV.
3D staking integration Neuromorphic architectures are fundamentally 3D
structures with massive parallelism, but the 2D planar
circuits on which they are implemented considerably limit
the bandwidth between layers (or significantly increase the
area and energy cost required to achieve sufficient
bandwidth). Indeed, neural networks typically exhibit a high
level of connectivity. Especially when biologically relevant
neural networks are considered, a given neuron can receive
information from up to 10.000 neurons. Deep computation
and cognitive functions may be reached with thousands of
neurons and millions of synapses [21][24][25][26].
In this section, we assume that the neural network is
multilayer and densely-connected, and neurons are
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point-to-point connected (hardwired connections). Dense
hardware integration is, therefore, required for advanced
processing tasks. Analog neurons are compact circuits but
connecting thousands of them leads to routing and
throughput problems. The 3D architectures are expected to
reduce the routing congestion problem related to neuroninterconnect, increase the scalability of the network, reduce
critical paths length, and finally save circuit area and power.
(a)
(b)
Figure 8: 2D (a) and 3D (b) designs of a neural processor. The two
layers of the neural network are mapped onto separate silicon
layers. The mean wire-length is reduced, as well as the routing
complexity and the inter-neuron throughput.
The case study presented in this paper is a neural
processor with a densely connected 2-layers neural network
and aiming to recognize objects appearing in a 1000frame/svideo stream. The final objective is to execute complex tasks,
such as real-time surveillance (motion detection + image
filtering + shape recognition) with low-power and compact
circuitry. The 3D design offers more flexibility to meet
timing, area and power constraints. Figure 8 shows the block
diagrams of both 2D and 3D partitioning of the neural
processor design. The intensity of inter-blockcommunication is represented by the thickness of edges
(thick edges for intense communication). The routing
congestion risk is represented using colors (red edges for
higher congestion risk to green edges for the opposite). In
2D design, the communication between the two neuron
layers is the most intensive in terms of data exchange and
throughput requirements. This complexity is mitigated after
mapping the network layers onto separate silicon layers for
3D design. Throughput and congestion problems are,
interestingly, alleviated by the increasing number of metal
layers and short connections created by 3D routing.
The design methodology we used consists on designing
two separate circuits for face-to-face staking fabrication process. We use two types of TSV to link both circuits:
TSVs that are created by bonding the last metal layers of
both circuits (bumps), and TSVs that cross all the metal
layers to output pad signals of the hidden circuit (IO TSV).
Bumps have a size of 5x5!m2 and may be placed
everywhere in the circuit body, while IO TSVs have a size
of a 60x90!m2 and can only be placed in the corona area of
both circuits.
A 2-tier 3D layout has been built in a 130nm technology.
Layouts show a decrease in the mean wire-length. Short
connections reduce the footprint area in the 3D design,
which is largely related to the buffer count reduction
because of shorter wire-length and hence better timing.Routing demand is quite different between 2D and 3D
designs. As for the 2D case, a large number of inter-neuron
and over-neuron connections are required, and this increases
both total and average wire-length. Thus, more high metal
layers are necessary to complete inter-neuron routing. In the
other hand, many wires in the 3D design are connected to
nearby bumps, and this reduces wiring demand significantly.
Figure 9 shows the mapping results of the same neural
network in 2D (left) and 3D (right). 3D mapping allows to
tremendously decrease the total connections length, and
hence the associated power consumption.
Figure 9: mapping results of the same neural network (184 neurons,
1181 connections) in 2D (left) and 3D (right). Total connections
length is reduced by 3x.
Configuration
Memory
41 2 3Controler
Layer 1
Controler
Layer 2
Switcher 1
5 m
41 2 3 5 n
IF
…
…
Switcher 2 … Switcher m
Memory
Layer 1
Layer 2
Memory
41 2 3Controler
Layer 1
Switcher 1
5 m
IF
…
Switcher 2 … Switcher m
Configuration
Controler
Layer 2
41 2 3 5 n…
Memory
Bump matrix
Bump matrix
TSV pad
TSV pad
Layer 1
Layer 2
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V. 3D monolithic integration3D-TSV opens the way towards more integration thanks
to new functions (3D-capacitances) and reduced wire
lengths as demonstrated in previous sections. However,
recent ITRS [27] roadmap shows that TSV alignment will be
limited between ~ 0.5!m and 1µm to guarantee correctoperation (Table I). This is due to the TSV process which is
performed outside the foundry, thus not benefiting from
advanced lithography.
Due to the restriction in the alignment, the pitch between
two TSVs cannot be smaller than ~ 4 - 8µm. In addition,
height of TSV (~ 20 - 50 µm) implies performance impact
when signals crossing TSV are in the chip critical paths. As
we have seen before, high capacitance can be used for
neurons design. However, the limited number of vertical
TSV per mm2 prevent from using this technology for
ultra-dense wires.
Recently, a new 3D technology called 3D Monolithic
Integration (3DMI) has appeared. With 3DMI, transistorlayers are fabricated one after another on the same die using
high-end lithography equipment. It results in an improved
alignment performance of ~10nm [28].
Therefore, vertical connections can be placed with a very
small footprint of less than 100nm diameter in 65nm
technology [29].
Table 1. 3DMI vs 3D-TSV comparison
Alignment
(!m)
Diameter
(!m)
Pitch
(!m)
Minimum
height
(!m)
TSV 0.5 - 1 2 - 4 4 - 8 20 – 50
3DMI 0.01 0.1 0.2* 0.1
3DMI vs
TSV gain
50x to
100x
20x to
40x
20x to
40x
200x to
500x
* Pitch is assumed to be at least two times larger than the
diameter.
Moreover, the distance between the top and bottom layers
can be reduced to 100nm [28]. Consequently the
performance while passing through the vertical via is greatly
improved. As a result, efficient, very fine grain partitioning
is achievable with 3DMI. Figure 10 shows some partitioning
possibilities based on 3DMI integration. Contrary to TSV,the 3D connections are on the same size than classical 2D
vias. No guard interval is required and classical design rules
can be used. This characteristic allows ultra-fine grain
partitioning: as shown in Figure 10.a, a classical library cell
can be split in two layers. Figure 10.b shows a so-called
“cell-on-cell” approach requiring new 3D place & route
tools with a fine grain partitioning [30]. In all cases, 3DMI
becomes an ideal choice for highly integrated 3D circuits.
(a)
(b)
Figure 10 Monolithic 3D integration approaches, (a)
transistor-level (N/P), (b) gate-level (cell-on-cell)
3DMI implementation of NN has not been demonstrated
yet. However, compared to 3D TSV, a high degree of
improvement is expected, especially when considering
multi-layers of neurons which is not an issue with 3DMI. If
we consider that NN are working at quite low frequency
compared to classical devices, thermal issues should not be a
problem.
VI. ConclusionIn this paper, we have presented the usage of emerging
technologies for greatly improve the implementation of
Neural Networks: Synapses effects can be obtained by
Memristors thanks to PCM or CBRAM. Neurons cost can be
reduced by mixing analog design and high TSV capacitances.
Finally, neurons connections issues can be partially solved
through 3D integration, monolithic integration being the
most advanced case.
All these technologies appear at the same time, and when
mixed with new “smart” and low-power applications
requirements, we believe we have here a unique opportunityof pushing NN forward.
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