62 research outputs found
Potential and Challenges of Analog Reconfigurable Computation in Modern and Future CMOS
In this work, the feasibility of the floating-gate technology in analog computing platforms in a scaled down general-purpose CMOS technology is considered. When the technology is scaled down the performance of analog circuits tends to get worse because the process parameters are optimized for digital transistors and the scaling involves the reduction of supply voltages. Generally, the challenge in analog circuit design is that all salient design metrics such as power, area, bandwidth and accuracy are interrelated. Furthermore, poor flexibility, i.e. lack of reconfigurability, the reuse of IP etc., can be considered the most severe weakness of analog hardware. On this account, digital calibration schemes are often required for improved performance or yield enhancement, whereas high flexibility/reconfigurability can not be easily achieved. Here, it is discussed whether it is possible to work around these obstacles by using floating-gate transistors (FGTs), and analyze problems associated with the practical implementation. FGT technology is attractive because it is electrically programmable and also features a charge-based built-in non-volatile memory. Apart from being ideal for canceling the circuit non-idealities due to process variations, the FGTs can also be used as computational or adaptive elements in analog circuits.
The nominal gate oxide thickness in the deep sub-micron (DSM) processes is too thin to support robust charge retention and consequently the FGT becomes leaky. In principle, non-leaky FGTs can be implemented in a scaled down process without any special masks by using “double”-oxide transistors intended for providing devices that operate with higher supply voltages than general purpose devices. However, in practice the technology scaling poses several challenges which are addressed in this thesis.
To provide a sufficiently wide-ranging survey, six prototype chips with varying complexity were implemented in four different DSM process nodes and investigated from this perspective. The focus is on non-leaky FGTs, but the presented autozeroing floating-gate amplifier (AFGA) demonstrates that leaky FGTs may also find a use. The simplest test structures contain only a few transistors, whereas the most complex experimental chip is an implementation of a spiking neural network (SNN) which comprises thousands of active and passive devices. More precisely, it is a fully connected (256 FGT synapses) two-layer spiking neural network (SNN), where the adaptive properties of FGT are taken advantage of. A compact realization of Spike Timing Dependent Plasticity (STDP) within the SNN is one of the key contributions of this thesis.
Finally, the considerations in this thesis extend beyond CMOS to emerging nanodevices. To this end, one promising emerging nanoscale circuit element - memristor - is reviewed and its applicability for analog processing is considered. Furthermore, it is discussed how the FGT technology can be used to prototype computation paradigms compatible with these emerging two-terminal nanoscale devices in a mature and widely available CMOS technology.Siirretty Doriast
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Surface electromyogram (sEMG) signals result from muscle movement and hence
they are an ideal candidate for benchmarking event-driven sensing and
computing. We propose a simple yet novel approach for optimizing the spike
encoding algorithm's hyper-parameters inspired by the readout layer concept in
reservoir computing. Using a simple machine learning algorithm after spike
encoding, we report performance higher than the state-of-the-art spiking neural
networks on two open-source datasets for hand gesture recognition. The spike
encoded data is processed through a spiking reservoir with a biologically
inspired topology and neuron model. When trained with the unsupervised activity
regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir
yields better performance than state-of-the-art convolutional neural networks.
The reservoir performance with regulated activity was found to be 89.72% for
the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset.
Therefore, the biologically-inspired computing paradigm, which is known for
being power efficient, also proves to have a great potential when compared with
conventional AI algorithms.Comment: Accepted to International Conference on Neuromorphic Systems (ICONS
2021
Analog VLSI Circuits for Biosensors, Neural Signal Processing and Prosthetics
Stroke, spinal cord injury and neurodegenerative diseases such as ALS and Parkinson's debilitate their victims by suffocating, cleaving communication between, and/or poisoning entire populations of geographically correlated neurons. Although the damage associated with such injury or disease is typically irreversible, recent advances in implantable neural prosthetic devices offer hope for the restoration of lost sensory, cognitive and motor functions by remapping those functions onto healthy cortical regions. The research presented in this thesis is directed toward developing enabling technology for totally implantable neural prosthetics that could one day restore lost sensory, cognitive and motor function to the victims of debilitating neural injury or disease.
There are three principal components to this work. First, novel integrated biosensors have been designed and implemented to transduce weak extra-cellular electrical potentials and optical signals from cells cultured directly on the surface of the sensor chips, as well as to manipulate cells on the surface of these chips. Second, a method of detecting and identifying stereotyped neural signals, or action potentials, has been mapped into silicon circuits which operate at very low power levels suitable for implantation. Third, as one small step towards the development of cognitive neural implants, a learning silicon synapse has been implemented and a neural network application demonstrated.
The original contributions of this dissertation include:
* A contact image sensor that adapts to background light intensity and can asynchronously detect statistically significant optical events in real-time;
* Programmable electrode arrays for enhanced electrophysiological recording, for directing cellular growth, for site-specific in situ bio-functionalization, and for analyte and particulate collection;
* Ultra-low power, programmable floating gate template matching circuits for the detection and classification of neural action potentials;
* A two transistor synapse that exhibits spike timing dependent plasticity and can implement adaptive pattern classification and silicon learning
Asynchronous spike event coding scheme for programmable analogue arrays and its computational applications
This work is the result of the definition, design and evaluation of a novel method to interconnect
the computational elements - commonly known as Configurable Analogue Blocks (CABs) - of
a programmable analogue array. This method is proposed for total or partial replacement of the
conventional methods due to serious limitations of the latter in terms of scalability.
With this method, named Asynchronous Spike Event Coding (ASEC) scheme, analogue signals
from CABs outputs are encoded as time instants (spike events) dependent upon those signals
activity and are transmitted asynchronously by employing the Address Event Representation
(AER) protocol. Power dissipation is dependent upon input signal activity and no spike events
are generated when the input signal is constant.
On-line, programmable computation is intrinsic to ASEC scheme and is performed without additional
hardware. The ability of the communication scheme to perform computation enhances
the computation power of the programmable analogue array. The design methodology and a
CMOS implementation of the scheme are presented together with test results from prototype
integrated circuits (ICs)
Digital pulse processing
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-74).This thesis develops an exact approach for processing pulse signals from an integrate-and-fire system directly in the time-domain. Processing is deterministic and built from simple asynchronous finite-state machines that can perform general piecewise-linear operations. The pulses can then be converted back into an analog or fixed-point digital representation through a filter-based reconstruction. Integrate-and-fire is shown to be equivalent to the first-order sigma-delta modulation used in oversampled noise-shaping converters. The encoder circuits are well known and have simple construction using both current and next-generation technologies. Processing in the pulse-domain provides many benefits including: lower area and power consumption, error tolerance, signal serialization and simple conversion for mixed-signal applications. To study these systems, discrete-event simulation software and an FPGA hardware platform are developed. Many applications of pulse-processing are explored including filtering and signal processing, solving differential equations, optimization, the minsum / Viterbi algorithm, and the decoding of low-density parity-check codes (LDPC). These applications often match the performance of ideal continuous-time analog systems but only require simple digital hardware. Keywords: time-encoding, spike processing, neuromorphic engineering, bit-stream, delta-sigma, sigma-delta converters, binary-valued continuous-time, relaxation-oscillators.by Martin McCormick.S.M
An implantable micro-system for neural prosthesis control and sensory feedback restoration in amputees
In this work, the prototype of an electronic bi-directional interface between the Peripheral
Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is
composed of two Integrated Circuits (ICs): a standard CMOS device for neural recording and
a High Voltage (HV) CMOS device for neural stimulation. The integrated circuits have been
realized in two different 0.35μm CMOS processes available fromAustriaMicroSystem(AMS).
The recoding IC incorporates 8 channels each including the analog front-end and the A/D
conversion based on a sigma delta architecture. It has a total area of 16.8mm2 and exhibits
an overall power consumption of 27.2mW. The neural stimulation IC is able to provide biphasic
current pulses to stimulate 8 electrodes independently. A voltage booster generates a
17V voltage supply in order to guarantee the programmed stimulation current even in case
of high impedances at the electrode-tissue interface in the order of tens of k. The stimulation
patterns, generated by a 5-bit current DAC, are programmable in terms of amplitude,
frequency and pulse width. Due to the huge capacitors of the implemented voltage boosters,
the stimulation IC has a wider area of 18.6mm2. In addition, a maximum power consumption
of 29mW was measured. Successful in-vivo experiments with rats having a TIME
electrode implanted in the sciatic nerve were carried out, showing the capability of recording
neural signals in the tens of microvolts, with a global noise of 7μVrms , and to selectively
elicit the tibial and plantarmuscles using different active sites of the electrode.
In order to get a completely implantable interface, a biocompatible and biostable package
was designed. It hosts the developed ICs with the minimal electronics required for their
proper operation. The package consists of an alumina tube closed at both extremities by
two ceramic caps hermetically sealed on it. Moreover, the two caps serve as substrate for
the hermetic feedthroughs to enable the device powering and data exchange with the external
digital controller implemented on a Field-Programmable Gate Array (FPGA) board. The
package has an outer diameter of 7mm and a total length of 26mm. In addition, a humidity
and temperature sensor was also included inside the package to allow future hermeticity
and life-time estimation tests.
Moreover, a wireless, wearable and non-invasive EEG recording system is proposed in order
to improve the control over the artificial limb,by integrating the neural signals recorded from
the PNS with those directly acquired from the brain. To first investigate the system requirements,
a Component-Off-The-Shelf (COTS) device was designed. It includes a low-power 8-
channel acquisition module and a Bluetooth (BT) transceiver to transmit the acquired data
to a remote platform. It was designed with the aimof creating a cheap and user-friendly system
that can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates and with a maximum
bandwidth of 524Hz. It was realized on a 19cm2 custom PCB with a maximum power consumption
of 270mW
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