Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - Fun-COMP (Functionally scaled computing technology: From novel devices to non-von Neumann architectures and algorithms for a connected intelligent world)

Teaser

The Fun-COMP project aims to develop a new wave of industry-relevant technologies that will extend the limits facing mainstream processing and storage approaches. We will do this by delivering innovative nanoelectronic and nanophotonic devices and systems that fuse together...

Summary

The Fun-COMP project aims to develop a new wave of industry-relevant technologies that will extend the limits facing mainstream processing and storage approaches. We will do this by delivering innovative nanoelectronic and nanophotonic devices and systems that fuse together the core information processing tasks of computing and memory, that incorporate in hardware the ability to learn adapt and evolve, that are designed from the bottom-up to take advantage of the huge benefits, in terms of increases in speed/bandwidth and reduction in power consumption, promised by the emergence of Silicon photonic systems. We will develop basic information processing building blocks that draw inspiration from biological approaches, providing computing primitives that can mimic the essential features of brain-like synapses and neurons to deliver a new foundation for fast, low-power, functionally-scaled computing based around non-von Neumann approaches. We will combine such computing primitives into reconfigurable integrated processing networks that can implement in hardware novel, intelligent, self-learning and adaptive computational approaches - including spiking neural networks, computing-in-memory and autonomous reservoir computing – and that are capable of addressing complex real-world computational problems in fast, energy-efficient ways. We will address the application of our novel technologies to future computing imperatives, including the analysis and exploitation of ‘big data’ and the ubiquity of computing arising from the ‘Internet of Things’. To realise our goals we bring together a world-leading consortium of industrial and academic researchers whose current work in the development of future information processing and storage technologies defines the state-of-the-art.

Fun-COMP\'s objectives can be summarised as developing functionally-enhanced computing devices and systems that
• Fuse together the core information processing tasks of computing and memory
• At the same time incorporate in hardware (not just in software) the ability to learn, adapt and evolve
• Are designed to take advantage of the huge benefits, in terms of both speed/bandwidth and power consumption, that is available via the use of photonic interconnects and photonic processing
More specifically we will
• Develop key novel computing hardware elements, including neuron and synapse mimics, binary and multilevel memories, arithmetic and computing-in-memory devices
• Combine these components together into architectures that deliver the fundamental building blocks of unconventional non-von-Neumann (N-vN) processors
• Combine these building blocks into topologies that can address
(i) difficult-to-solve (by conventional means) real-world problems -e.g. optimization, correlation, association in ‘big data’
(ii) provide localised, intelligent/adaptable, low-power computing nodes -e.g. for IoT applications.

Work performed

The work carried out so far, and the acheivements to date, can be summarised as follows:
• We have successfully fabricated a range of SiN and Si non-von Neumann (N-vN) integrated phase-change photonic devices and demonstrated binary and multilevel memory functionality, as well as arithmetic and neuromorphic processing capabilities.
• We have successfully developed (designed, fabricated and tested) a novel mixed-mode N-vN unit cell design in which both readout and switching can be achieved electrically or optically. Binary non-volatile memory functionality was successfully demonstrated using this mixed-mode device.
• We have developed and fabricated microring waveguide devices (i.e. N-vN extended unit cell devices), both with and without integrated phase-change cells, and exploited such devices to provide (i) wavelength division multiplexed coupling devices, (ii) mimics of spiking neurons and (iii) scalable memory and neuromorphic architectures
• We have designed optically pumped (OP) nanolasers for spiking operation based on a single photonic crystal cavity
• We have developed theoretical models for gain/loss coupled semiconductor nanocavities, for the understanding and the prediction of self-pulsing neuron-like regimes.
• We have fabricated the1st generation of OP nanolasers for spiking operation based on a single photonic crystal cavity and coupled nanocavities.
• We have developed a 3D finite element model and (faster, simpler) behavioural models capable of simulating the write, erase and read processes in the N-vN devices.
• We have developed a computationally efficient model describing the nonlinear dynamics of III-V/Silicon hybrid nanolasers
• We have combined Fun-COMP devices (or computing primitives) to deliver (i) small-scale all-optical memory chips , (ii) small-scale all-optical neuromorphic processing chips and (iii) photonic arrays suited to carrying out arithmetic processing (specifically matrix-vector multiplication)
• We have developed successful approaches to allow transfer of N-vN device and system design to standard Si-photonics fabrication (via ePIXfab Europractice runs)
• We have designed a number of photonic system components, specifically multiplexers and modulators, to allow for the development of re-configurable N-vN computing platforms on the imec (integrated photonics) platform

Final results

The Fun-COMP consortium continues to define the state-of-the-art in the area of intergrated phase-change photonic computing.
In this first 18 month period we have published a total of 13 journal papers, including contiributions to the most prestigious journals (e.g,. Nature, Science Advances, Advanced Materials, Advanced Functional Materials, Nano Letters and Optica), and have made 20 presentations at international conferences/workshops (10 of which were invited).
We have developed novel techniques and devices for a number of key computational operations/techniques including (i) all-opitcal spiking neural networks, (ii) photonic matrix-vector multiplication arrays, (iii) multi-bit non-volatile photonic memories.
In the remaining project period(s) we expect to deliver small-scale photonic and mixed-mode (optical-electrical) chips capable of carrying out important computational tasks (such as covolutional processing, image recognition, object identification etc.) in a fast, low-power manner.
Ultimately, Fun-COMP aims to deliver impact by exploiting the massive gains in speed and power consumption promised by silicon photonics configured in a non-von Neumann computing format, so that computing intelligence is ‘built-in’ to the hardware and is ideally suited to new approaches to problem solving that use adaptive and self-learning techniques, including spiking neural networks, reservoir computing and computing-in-memory.

Website & more info

More info: http://www.fun-comp.org.