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”In contemporary electronics 80% of a chip may perform digital functions but the 20%
of analog functions may take 80% of the development time.” [1]. Aggravating this, the
demands on analog design is increasing with rapid technology scaling. Most designs
have moved away from analog to digital domains, where possible, however, interacting
with the environment will always require analog to digital data conversion. Adding to
this problem, the number of sensors used in consumer and industry related products are
rapidly increasing. Designers of ADCs are dealing with this problem in several ways, the
most important is the migration towards digital designs and time domain techniques.
Time to Digital Converters (TDC) are becoming increasingly popular for robust signal
processing. Biological neurons make use of spikes, which carry spike timing information
and will not be affected by the problems related to technology scaling. Neuromorphic
ADCs still remain exotic with few implementations in sub-micron technologies Table 2.7.
Even among these few designs, the strengths of biological neurons are rarely exploited.
From a previous work [2], LUCOS, a high dynamic range image sensor, the efficiency
of spike processing has been validated. The ideas from this work can be generalized to
make a highly effective sensor signal conditioning system, which carries the promise to
be robust to technology scaling.
The goal of this work is to create a novel spiking neural ADC as a novel form of a
Multi-Sensor Signal Conditioning and Conversion system, which
• Will be able to interface with or be a part of a System on Chip with traditional
analog or advanced digital components.
• Will have a graceful degradation.
• Will be robust to noise and jitter related problems.
• Will be able to learn and adapt to static errors and dynamic errors.
• Will be capable of self-repair, self-monitoring and self-calibration
Sensory systems in humans and other animals analyze the environment using several
techniques. These techniques have been evolved and perfected to help the animal sur-
vive. Different animals specialize in different sense organs, however, the peripheral
neural network architectures remain similar among various animal species with few ex-
ceptions. While there are many biological sensing techniques present, most popularly
used engineering techniques are based on intensity detection, frequency detection, and
edge detection. These techniques are used with traditional analog processing (e.g., colorvi
sensors using filters), and with biological techniques (e.g. LUCOS chip [2]). The local-
ization capability of animals has never been fully utilized.
One of the most important capabilities for animals, vertebrates or invertebrates, is the
capability for localization. The object of localization can be predator, prey, sources of
water, or food. Since these are basic necessities for survival, they evolve much faster
due to the survival of the fittest. In fact, localization capabilities, even if the sensors
are different, have convergently evolved to have same processing methods (coincidence
detection) in their peripheral neurons (for e.g., forked tongue of a snake, antennae of
a cockroach, acoustic localization in fishes and mammals). This convergent evolution
increases the validity of the technique. In this work, localization concepts based on
acoustic localization and tropotaxis are investigated and employed for creation of novel
ADCs.
Unlike intensity and frequency detection, which are not linear (for e.g. eyes saturate in
bright light, loose color perception in low light), localization is inherently linear. This
is mainly because the accurate localization of predator or prey can be the difference
between life and death for an animal.
Figure 1 visually explains the ADC concept proposed in this work. This has two parts.
(1) Sensor to Spike(time) Conversion (SSC), (2) Spike(time) to Digital Conversion(SDC).
Both of the structures have been designed with models of biological neurons. The
combination of these two structures is called SSDC.
To efficiently implement the proposed concept, a comparison of several biological neural
models is made and two models are shortlisted. Various synapse structures are also
studied. From this study, Leaky Integrate and Fire neuron (LIF) is chosen since it
fulfills all the requirements of the proposed structure. The analog neuron and synapse
designs from Indiveri et. al. [3], [4] were taken, and simulations were conducted using
cadence and the behavioral equivalence with biological counterpart was checked. The
LIF neuron had features, that were not required for the proposed approach. A simple
LIF neuron stripped of these features and was designed to be as fast as allowed by the
technology.
The SDC was designed with the neural building blocks and the delays were designed
using buffer chains. This SDC converts incoming Time Interval Code (TIC) to sparse
place coding using coincidence detection. Coincidence detection is a property of spiking
neurons, which is a time domain equivalent of a Gaussian Kernel. The SDC is designed to
have an online reconfigurable Gaussian kernel width, weight, threshold, and refractory
period. The advantage of sparse place codes, which contain rank order coding wasvii
Figure 1: ADC as a localization problem (right), Jeffress model of sound localization
visualized (left). The values t 1 and t 2 indicate the time taken from the source to s1 and
s2 respectively.
described in our work [5]. A time based winner take all circuit with memory was created
based on a previous work [6] for reading out of sparse place codes asynchronously.
The SSC was also initially designed with the same building blocks. Additionally, a
differential synapse was designed for better SSC. The sensor element considered wasviii
a Wheatstone full bridge AMR sensor AFF755 from Sensitec GmbH. A reconfigurable
version of the synapse was also designed for a more generic sensor interface.
The first prototype chip SSDCα was designed with 257 modules of coincidence detectors
realizing the SDC and the SSC. Since the spike times are the most important information,
the spikes can be treated as digital pulses. This provides the capability for digital
communication between analog modules. This creates a lot of freedom for use of digital
processing between the discussed analog modules. This advantage is fully exploited
in the design of SSDCα. Three SSC modules are multiplexed to the SDC. These SSC
modules also provide outputs from the chip simultaneously. A rising edge detecting fixed
pulse width generation circuit is used to create pulses that are best suited for efficient
performance of the SDC. The delay lines are made reconfigurable to increase robustness
and modify the span of the SDC. The readout technique used in the first prototype is
a relatively slow but safe shift register. It is used to analyze the characteristics of the
core work. This will be replaced by faster alternatives discussed in the work. The area
of the chip is 8.5 mm 2 . It has a sampling rate from DC to 150 kHz. It has a resolution
from 8-bit to 13-bit. It has 28,200 transistors on the chip. It has been designed in 350
nm CMOS technology from ams. The chip has been manufactured and tested with a
sampling rate of 10 kHz and a theoretical resolution of 8 bits. However, due to the
limitations of our Time-Interval-Generator, we are able to confirm for only 4 bits of
resolution.
The key novel contributions of this work are
• Neuromorphic implementation of AD conversion as a localization problem based
on sound localization and tropotaxis concepts found in nature.
• Coincidence detection with sparse place coding to enhance resolution.
• Graceful degradation without redundant elements, inherent robustness to noise,
which helps in scaling of technologies
• Amenable to local adaptation and self-x features.
Conceptual goals have all been fulfilled, with the exception of adaptation. The feasibility
for local adaptation has been shown with promising results and further investigation is
required for future work. This thesis work acts as a baseline, paving the way for R&D
in a new direction. The chip design has used 350 nm ams hitkit as a vehicle to prove
the functionality of the core concept. The concept can be easily ported to present
aggressively-scaled-technologies and future technologies.