509 research outputs found
Concepts and Paradigms for Neuromorphic Programming
The value of neuromorphic computers depends crucially on our ability to
program them for relevant tasks. Currently, neuromorphic computers are mostly
limited to machine learning methods adapted from deep learning. However,
neuromorphic computers have potential far beyond deep learning if we can only
make use of their computational properties to harness their full power.
Neuromorphic programming will necessarily be different from conventional
programming, requiring a paradigm shift in how we think about programming in
general. The contributions of this paper are 1) a conceptual analysis of what
"programming" means in the context of neuromorphic computers and 2) an
exploration of existing programming paradigms that are promising yet overlooked
in neuromorphic computing. The goal is to expand the horizon of neuromorphic
programming methods, thereby allowing researchers to move beyond the shackles
of current methods and explore novel directions
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
This work describes the investigation of neuromorphic computing-based spiking
neural network (SNN) models used to filter data from sensor electronics in high
energy physics experiments conducted at the High Luminosity Large Hadron
Collider. We present our approach for developing a compact neuromorphic model
that filters out the sensor data based on the particle's transverse momentum
with the goal of reducing the amount of data being sent to the downstream
electronics. The incoming charge waveforms are converted to streams of
binary-valued events, which are then processed by the SNN. We present our
insights on the various system design choices - from data encoding to optimal
hyperparameters of the training algorithm - for an accurate and compact SNN
optimized for hardware deployment. Our results show that an SNN trained with an
evolutionary algorithm and an optimized set of hyperparameters obtains a signal
efficiency of about 91% with nearly half as many parameters as a deep neural
network.Comment: Manuscript accepted at ICONS'2
Design of the Artificial: lessons from the biological roots of general intelligence
Our desire and fascination with intelligent machines dates back to the
antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
(syllogism) and Heron of Alexandria's mechanical machines and automata.
However, the quest for Artificial General Intelligence (AGI) is troubled with
repeated failures of strategies and approaches throughout the history. This
decade has seen a shift in interest towards bio-inspired software and hardware,
with the assumption that such mimicry entails intelligence. Though these steps
are fruitful in certain directions and have advanced automation, their singular
design focus renders them highly inefficient in achieving AGI. Which set of
requirements have to be met in the design of AGI? What are the limits in the
design of the artificial? Here, a careful examination of computation in
biological systems hints that evolutionary tinkering of contextual processing
of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence
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