Introducing AIRAWAT (AI Research, Analytics and knowledge Assimilation platform)
National Mission for Interdisciplinary Cyber Physical Systems (NM-ICPS), have emphasized the need for enhancing both the core and applied research capabilities in AI, through initiatives like setting up of COREs (Centers of Research Excellence), ICTAIs (International Centers Transformational AI) and Innovation Hubs.
In addition several other initiatives are being taken by governments and private sector to increase the adoption of AI, both in governance and private enterprises. These initiatives would spur the demand and necessity for state-of-the-art and specialised AI computing infrastructure. In order to meet this demand and tackle the challenges associated with lack of access to computing resources highlighted, it is proposed that an AI-specific compute infrastructure be established. Such an infrastructure will power the computing needs of COREs, ICTAIs and Innovation Hubs, as well as facilitate the work of broader spectrum of stakeholders in the AI research and application ecosystem (startups, researchers, students, government organizations, etc.).
The proposal to establish India’s own AI-first compute infrastructure is aimed to facilitate and speed up research and solution development for solving India’s societal challenges using high performance and high throughput AI-specific supercomputing technologies. The key design considerations for this infrastructure are:
Institutional framework for implementation: an interdisciplinary task force Structure of the facility: whether it should be centralized (in a single location), decentralized (access from across multiple locations) or utilize existing infrastructure (through existing Cloud Service Providers or existing HPC infrastructure); Modes of access: whether it should be made available similar to access mechanisms for a traditional HPC or through as a fully managed cloud service; Architecture of facility: what would constitute the roader technical design considerations; The proposed infrastructure is acronymed AIRAWAT, i.e. the “AI Research, Analytics and knowledge Assimilation platform”) and the design suggested is in line with the recommendations of the NSAI.
NSM envisages developing a supercomputing grid of more than 70 high-performance computing facilities. AIRAWAT is expected to complement the infrastructure developed for NSM, with specific focus on AI computing. It is proposed that AIRAWAT consider a similar approach of cloud-based access, given the often flexible requirements of AI compute tasks. With regard to broader considerations of access, AIRAWAT may be made accessible to users across the country through National Knowledge Network (NKN), for which NKN may need to be upgraded suitably.
The proposed architecture, with composite compute and storage infrastructure allows maintaining large data sets (thus eliminating the need for separate data centres and addressing data integrity concerns), and proximity of compute facility for efficient processing of data-intensive tasks viz. training of algorithms on large (both number and size) datasets.
The expected infrastructure, with capabilities of more than 100 peta flops (in the simplest sense, an AI flop is a measure of how fast a computer can perform deep neural network operations), would be more than the combined computing facility of top 20 supercomputers in India, and will put India on the global AI map, at par with the likes of Europe and Japan. Energy efficiency will be a key aspect of the facility, with the aim of putting AIRWAT in the list of top global green supercomputers. The facility would also enable storing of India’s massive data sets from areas like healthcare, agriculture locally in a high throughput and efficient storage.
This new centralised AI infrastructure would alleviate any data sharing concerns (eliminating need to share data at multiple decentralised locations), is aimed at reusing existing high bandwidth infrastructure (e.g. NKN), is a better approach to utilization of computing resources in multi-user and multi-tenant environment, and has the scaling flexibility to include both small experiments as well as solving grand challenges / big data. The use cases for AIRAWAT may vary from Big Data Analytics to specialised AI solutions across multiple domains viz. Healthcare (precision diagnostics, non-invasive diagnostics etc.), Agriculture (precision agriculture, crop infestations, advanced agronomic advisory etc.), weather forecasting, security and surveillance, financial inclusion and other services (fraud detection), infrastructural tools viz. NLP etc.
