BrainChip
Ritik Shrivastava is a skilled Machine Learning Solutions Architect currently at BrainChip since June 2023, focusing on developing tailored machine learning solutions primarily in the audio domain using technologies like PyTorch and TensorFlow while specializing in model optimization for the Akida Neuromorphic Chip. Prior experience includes serving as Technical Lead at NSIN, where standardization of requirement markup tags and development of AIML natural language processing were key projects. As a Data Engineer at InfoObjects Inc., Ritik streamlined data contract creation and optimized data ingestion processes, collaborating closely with stakeholders. Additional contributions were made as a Junior Software Developer at NUCLEON AI PRIVATE LIMITED, where a chatbot framework was developed and predictive models were implemented to enhance client inventory management. Ritik holds a Master of Science in Electrical and Computer Engineering from the University of Washington and a Bachelor of Engineering from the Institute of Engineering & Technology DAVV.
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BrainChip
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BrainChip is a global technology company that has developed a revolutionary advanced neural networking processor that brings artificial intelligence to the edge in a way that existing technologies are not capable. The solution is high performance, small, ultra-low power and enables a wide array of edge capabilities that include local training, learning and inference. The company markets an innovative event-based neural network processor that is inspired by the spiking nature of the human brain and implements the network processor in an industry standard digital process. By mimicking brain processing, BrainChip has pioneered a spiking neural network, called Akida™, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than transmission to the cloud or a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint data centers.