http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
패턴 선별을 이용한 패턴 학습 TCAM 메모리 구조 개선 방법의 구현
김경민(Kyeong-Min Kim),한재훈(Jae-Hun Han),강승만(Seung-Man Kang),방민경(Min-Kyung Bang),박성배(Seong-Bae Park),홍충선(Choong-Seon Hong),홍상훈(Sang-Hoon Hong) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
This is a paper on the circuit implementation to improve the PIM artificial intelligence processor that recognizes patterns using TCAM. This method separates the memory space into minor and major, and even though the memory capacity is reduced by 84.4%, the accuracy can be maintained almost. Since the method of reading and writing memory data for separation causes a waste of time, time and area are saved by implementing the method through a shift register and D flip-flop. Using this method, it is possible to reduce the amount of memory used and to simplify the method of controlling it.
패턴 학습이 가능한 TCAM 메모리를 이용한 비지도 학습
박소희(So-Hee Park),방민경(Min-kyung Bang),이언경(Eon-gyeong Lee),한재훈(Jae-Hun Han),구원모(Won-mo Koo),박성배(Seong-Bae Park),홍충선(Choong-Seon Hong),홍상훈(Sang-Hoon Hong) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Processing in memory (PIM) is an attention drawing technology that provides a solution to the von Neumann bottleneck, which occurs in data transmissions between the CPU and memory, providing massive improvement in the overall data processing performance. In this paper, we propose unsupervised learning algorithm using the PIM artificial intelligence processors over TCAM. By using the data preprocessing of thinning and thickening, the MNIST data has clustered with 88.74% accuracy. It displays an advantage in that it consists of high accuracy even with a simple algorithm.