2021/03/30 by Alex Pappachen James · 1 citation
Engineering · Neuroscience · #Advanced Memory and Neural Computing #Ferroelectric and Negative Capacitance Devices #Neuroscience and Neural Engineering #Computer science #Scalability #Artificial intelligence #Implementation #Automation #Robustness (evolution) #Artificial general intelligence #Machine learning #Software engineering #Database #Engineering
paper · doi:10.1109/tcds.2021.3069871
openalex publication_date 2021/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2025/11/06
The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities. Such a requirement to transit from application-specific to general intelligence AI chip must consider several factors. This article provides an overview of this cross-disciplinary field of study, elaborating on the generalisation of intelligence as understood in building artificial general intelligence (AGI) systems. This work presents a listing of emerging AI chip technologies, classification of edge AI implementations, and the funnel design flow for AGI chip development. Finally, the design consideration required for building an AGI chip is listed along with the methods for testing and validating it.