An extensive reliance on technology, an abundance of data, and increasing processing
requirements have imposed severe challenges on computing and data processing.
Moreover, the roadmap for scaling electronic components faces physical and reliability
limits that hinder the utilization of the transistors in conventional systems and promotes
the need for faster, energy-efficient, and compact nano-devices. This work thus
capitalizes on emerging non-volatile memory technologies, particularly the memristor
for steering novel design directives. Moreover, aside from the conventional deterministic
operation, a temporal variability is encountered in the devices functioning. This
inherent stochasticity is addressed as an enabler for endorsing the stochastic electronics field of study. We tackle this approach of design by proposing and verifying a statistical approach to modelling the stochastic memristors behaviour. This mode of
operation allows for innovative computing designs within the approximate computing
and beyond Von-Neumann domains.
In the context of approximate computing, sacrificing functional accuracy for the
sake of energy savings is proposed based on inherently stochastic electronic components. We introduce mathematical formulation and probabilistic analysis for Boolean logic operators and correspondingly incorporate them into arithmetic blocks. Gate- and system-level accuracy of operation is presented to convey configurability and the different effects that the unreliability of the underlying memristive components has on the intermediary and overall output. An image compression application is presented
to reflect the efficiency attained along with the impact on the output caused by the
relative precision quantification.
In contrast, in neuromorphic structures the memristors variability is mapped onto
abstract models of the noisy and unreliable brain components. In one approach, we
propose using the stochastic memristor as an inherent source of variability in the
neuron that allows it to produce spikes stochastically. Alternatively, the stochastic
memristors are mapped onto bi-stable stochastic synapses. The intrinsic variation
is modelled as added noise that aids in performing the underlying computational
tasks. Both aspects are tested within a probabilistic neural network operation for a
handwritten MNIST digit recognition application. Synaptic adaptation and neuronal
selectivity are achieved with both approaches, which demonstrates the savings, interchangeability, robustness, and relaxed design space of brain-inspired unconventional computing systems.
|Date of Award||May 2017|
|Original language||English (US)|
- Computer, Electrical and Mathematical Sciences and Engineering
|Supervisor||Khaled Salama (Supervisor)|
- Approximate computing
- Stochastic electronics