Interesting paper well worth reading. Very easy to follow and not too technical:Abstract
This article provides a detailed comparative analysis of two advanced machine learning technologies: Amazon SageMaker and BrainChip Akida Neuromorphic Processor. Amazon SageMaker, a fully managed service from AWS, facilitates the building, training, and deployment of machine learning models at scale, supporting various frameworks and offering automated infrastructure management. Conversely, BrainChip Akida introduces a neuromorphic computing approach, designed to mimic human brain processes, thus enabling efficient, low-power AI computations ideal for edge devices. This analysis highlights the core features, applications, and operational implications of each technology. SageMaker excels in scalable, cloud-based environments requiring robust data handling and flexible computational resources, making it suitable for industries such as finance and healthcare. Akida, on the other hand, performs optimally in edge computing scenarios where power efficiency and rapid, local data processing are critical, such as in IoT devices and autonomous vehicles. The article discusses the suitability of each platform for specific use cases and examines how each technology meets different requirements in the expanding field of artificial intelligence and machine learning.
References
BrainChip Holdings Ltd. (2021). Akida Neuromorphic System-on-Chip Architecture. Retrieved from https://www.brainchipinc.com/akida-neuromorphic-system-chip-architecture/
Amazon Web Services. (2021). AWS Documentation on Amazon SageMaker. Retrieved from https://docs.aws.amazon.com/sagemaker/
Electronic Design.(2018) BrainChip unveils Akida Development Environment.Retrieved from https://www.electronicdesign.com/technologies/test-measurement/article/21208594/
Cass, S. (2019). "The Next Generation of AI Processors Goes Analog." IEEE Spectrum. Retrieved from https://spectrum.ieee.org/next-generation-ai-analog
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). "ImageNet Classification with Deep Convolutional Neural Networks." Communications of the ACM, 60(6), 84-90.
Marr, B. (2020). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.
National Institute of Standards and Technology. (2021). NIST on AI and Machine Learning Frameworks. Retrieved from https://www.nist.gov/topics/artificial-intelligence
Rajkomar, A., Dean, J., & Kohane, I. (2019). "Machine Learning in Medicine." The New England Journal of Medicine, 380, 1347-1358.
Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). "Green AI." Communications of the ACM, 63(12), 54-63.
The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, Version 2. IEEE.
Vellido, A., Martin-Guerrero, J. D., & Lisboa, P. J. G. (2012). "Making machine learning models interpretable." ESANN 2012 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 25-27 April 2012, i6doc.compubl., ISBN 978-2-87419-049-0.
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