WHY I STUDIED THIS
AI computation has a hardware problem — and neuromorphic engineering is one of the most radical attempts to solve it. I studied this to understand what comes after GPUs: how brain-inspired chip architectures (spiking neural networks, memristors, Nengo) could make AI dramatically more power-efficient and enable true edge/embedded AI. This connects directly to my work at NAVER Cloud, where inference efficiency and on-device AI were active strategic questions, and to the chip market analysis I did in the Tech x Product Trends research.
대한민국이 해냈습니다... 라는 KAIST 유회준 교수님 연구진의 뉴로모픽 반도체, 세계 최초 개발의 진짜 의미는 이겁니다.
Nengo (Scripting Interface) + Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics
Nengo (Scripting Interface) + Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics
https://github.com/mikeroyal/Neuromorphic-Computing-Guide
Neuromorphic Computing Guide
: including the applications, libraries and tools
Self Study
Type of Neural Networks
open platform