Not sure if posted previously but I see a couple of our TCS friends are presenting a Neuromorphic tutorial at the following upcoming conference.
Can presume they are working with various neuromorphic offerings(?) and I found a statement (highlighted / underlined) re real market apps to be interesting
2023 IEEE International Conference on Acoustics, Speech and Signal Processing
Mon Jun 5, PM
Presenters
Manan Suri (IIT Delhi), Sounak Dey (Tata Consultancy Services Ltd.), Arun M. George (TCS Research & Innovation)
INTRODUCTION
Embedding intelligence at the edge has become a critical requirement for many industry domains, especially disaster management, healthcare, manufacturing, retail, surveillance, remote sensing etc. Classical Machine learning or Deep learning (ML/DL) based systems, being heavy in terms of required computation and power consumption, are not suitable for Edge devices such as robot, drones, automated cars, satellites, routers, wearables etc. which are mostly battery driven and have very limited compute resource. Inspired from the extreme power efficiency of mammalian brains, an alternative computing paradigm of Spiking Neural Networks (SNN) also known as Neuromorphic Computing (NC), has evolved with a promise to bring in significant power efficiency compared to existing edge-AI solutions. NC follows non-von Neumann architecture where data and memory are collocated like brain neurons and SNNs handle only sparse event-based data (spikes) in asynchronous fashion. Inherently SNNs are very efficient to understand features in temporally varying signals and is found to efficiently classify/process auditory data, gestures/actions from video streams, spot keywords from audio streams, classify & predict time series from different sensors used in IoT, regenerate temporal patterns etc.
The community is pursuing multiple sophisticated dedicated Neuromorphic hardware platforms such as: Intel Loihi, IBM TrueNorth,
Brainchip Akida, SpiNNaker, DYNAPs to name a few.
Moreover, ultra-advanced and futuristic nanoelectronic devices and materials are being explored to build energy efficient neuromorphic computers. So this domain, as well as this tutorial, lines in the intersection of Computational Neuroscience, Machine Learning and In-memory Neuromorphic Computation techniques.
RATIONALE AND STRUCTURE OF THE TUTORIAL
The ICASSP community is at the forefront of research in the domain of signal processing. Thus it is extremely relevant to conduct a tutorial on advances in the domain of Neuromorphic Computing at the forum. We are proposing two valuable cross vertical elements in this tutorial:
(i) Firstly, the proposed tutorial has been developed keeping both academic and industrial/application interests in mind.
The speakers represent leading academic and industry research teams on the subject with several years of theoretical and applied experience on the topic. Over last few years, while solving customer requirements related to edge computing, TCS Research has successfully taken neuromorphic research to real market applications. At the same time, group at IIT-Delhi has contributed significantly towards development of cutting-edge neuromorphic hardware and memory-inspired computing.
(ii) Secondly, the proposed tutorial will not only cover foundational basics (i.e. algorithms, bio-inspiration, mathematics) of the subject, but will also delve in to real hardware-level implementation and actual application use-cases (such as gesture recognition in robotics, time series classification and prediction in IoT, continuous health monitoring, remote sensing via satellite etc.) as pursued in industry so far.
The tutorial is structured to cover all relevant aspects of SNN and NC as detailed below.
- Biological Background, Software & Simulation of SNNs:
We will start the tutorial by giving a background of modus operandi of biological neurons, their equivalent computational models, spike generation process, synaptic weight updates and learning rules. Next, we will speak in detail about (i) existing software tools and SNN simulators, (ii) how to create a basic SNN, feed data into it and do a simple classification task, (iii) how to tune it towards better performance.
Speaker: Sounak Dey, duration: 50 minutes.
- Neuromorphic Hardware Basics:
Second part of the tutorial will cover the basics of dedicated hardware approaches for implementing neuromorphic algorithms in real world. Key techniques will be bench-marked for their pros and cons. How specialized neuromorphic hardware can offer performance benefits will be discussed. The session will end with discussion of futuristic nanomaterials proposed for neuromorphic computation.
Speaker: Manan Suri, duration: 50 minutes.
- Application & Implementation:
Third and final part of the tutorial will provide a detailed landscape of the applications that have been developed and tested so far using SNN and NC, including our own experiences. Aspects such as spike encoding techniques, conversion of ANNs to SNNs, FPGAs will be discussed.
Speaker: Arun George, duration: 50 minutes.
A flexible and interactive model of discussion and Q-A with the audience will be followed throughout the tutorial. Duration:10-30 minutes
www.linkedin.com
thestockexchange.com.au
