This work presents a design of voltage-sensing Computation-in-Memory (CiM) using ferroelectric FET (FeFET) in the point of device and circuit for neuromorphic computing. FeFET CiM with Local Multiply and Global Accumulate (LM-GA) operation works for multiply–accumulate (MAC) of artificial neural networks (ANNs) and integrate operation of spiking neural networks (SNNs). The high scalability and high ON/OFF ratio of FeFETs contribute to large-capacity CiM for neural networks. For the device design, measured FeFET characteristics by source-follower read show small variation in read-disturb and data-retention. The circuit design of FeFET LM-GA CiM is discussed with evaluations of wiring capacitance and resistance, threshold voltage shift, and gate timing. In addition, integration operation of SNNs, which is not possible with current-sensing CiM, is possible with FeFET LM-GA CiM.

This paper proposes fast and small-area FeFET-based voltage-sensing analog Computation-in-Memory (CiM) for hyperdimensional computing (HDC) by eliminating large-scale digital circuit overhead. In both training and inference of HDC, MAP (bit-wise XOR, bit-wise majority rule, and 1-bit shift) of hypervectors (HVs) is operated by Partially added Text HV FeFET CiM and Text HV FeFET. In inference, Similarity search FeFET CiM obtains classification result. By taking an example of Language classification problem, the proposed voltage-sensing FeFET CiM for HDC encodes HV in training phase by 5,000 times faster and smaller area than the conventional method.

In this paper, we propose a content based video categorizing method focusing broadcasted sports videos using camera motion parameters. We define two new features in the proposed method; "camera motion extraction ratio" and "camera motion transition". Camera motion parameters in the video sequence contain very significant information for categorization of broadcasted sports video, because in most of sports video, camera motions are closely related to the actions taken in the sports, which are mostly based on a certain rule depending on types of sports. Based on the characteristics, we design a sports video categorization algorithm for identifying 6 major different sports types. In our algorithm, the features automatically extracted from videos are analyzed in a statistical manner. The experimental results show a clear tendency the applicability of the proposed method for sports genre identification.