Artificial intelligence (ai) and machine learning (ml) research has forced industries to create digital accelerators that can match the computational complexities required by advanced algorithms. however, these digital accelerators are computationally expensive and also area and energy inefficient. thus, there is a continuous need for improvising such systems for energy efficiency and performance. in this regard, analog accelerators can offer considerable benefits in terms of energy and area, but such solutions are not commercially available in the market. our innovation proposes an indigenously developed first-of-its-kind process technology, bias, and temperature scalable, low-cost analog ai accelerator. in contrast to the existing industry solution of digital accelerators, our innovation focuses on designing novel analog accelerators chip for achieving similar performance with lesser cost, power, and area. till date, no such indigenous accelerator exists in the market for analog ai applications. this technology has been named aryabhat-1 (analog reconfigurable technology and bias-scalable hardware for ai tasks). aryabhat-1 is not only an energy-efficient solution but can also be developed as a platform for learning analog computing which is a niche field with the potential to flip the existing market. while for digital system design and learning, educational institutions are equipped with fpgas (field programmable gate arrays), which are available at various costs, sizes, and computational capabilities, thus making the journey from textbook to smooth silicon experience in digital design. however, currently, no such reconfigurable analog computing platform is available that can facilitate learning and understanding of analog computing and circuit design on readily available hardware. analog engineers are expected to train themselves in numerous skills in order to equip themselves sufficiently for employment and research. thus, there is also a need to create exposure to such analog systems at the educational level to make our future generations industry-ready. our innovation tries to bridge this gap by proposing the first-ever analog computing ai platform for educational institutions and facilitating learning and research in analog computing, which has enormous potential. the technology of aryabhat-1 will be coupled with a complete toolchain, making the analog learning experience user-friendly for undergraduate and graduate study. this educational kit will be named kalam-1 (kit for analog learning and modeling). this innovation is developed in line with india's semiconductor mission to train people in the vlsi domain, which is a rare find. at present, no such indigenous accelerator exists in the market for analog ai applications. the developed accelerator/kit can also be used for ultra-low power internet-of-things (iot) applications (a market that is expected to be $27 billion worth by 2027 with around 80 billion interconnected iot devices) and machine learning tasks.