In Sri Lanka, most of the population uses the Sinhala Language as their first language to communicate and for documentation in most government departments. It is evident that the digitalization of the Sinhala Language is essential in a country like Sri Lanka. The specialty of Sinhalese characters is that they have very tiny differences in feature, and the number of different characters formed from the letters of the Sinhala alphabet and its elements is relatively high, leading to the classification among the Sinhala letters becoming quite a complex task. Previous proposed research case studies involved machine learning based feature detections related to rule-based theories and geometry features that had average accuracy rates, which indicate that further improvement is required with new features. Consequently, in this research paper, a Deep Learning Character Classification method for Sinhala OCR is proposed, which is for both Printed and Handwritten Sinhala texts as well as an Intelligent Sinhala Form Automation technique to read both answers and questions in an application to convert them into e-texts. The converted e-texts will be sharpened and fixed through a Sinhala Spelling & Grammar checking feature that is developed in the system more intelligently. In this research work, it was a success to obtain an overall accuracy level of more than 90% considering all components.

Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this article, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing. The proposed architecture is benchmarked to predict on real-world streaming data. The network's mean absolute percentage error on the mixed-signal system is 1.129 X lower compared to its baseline algorithm model. This reduction can be attributed to device non-idealities and probabilistic formation of synaptic connections. We demonstrate that the combined effect of Hebbian learning and network sparsity also plays a major role in extending the overall network lifespan. We also illustrate that the system offers 3.46 X reduction in latency and 77.02 X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node. By employing specific low power techniques, such as clock gating, we observe 161.37 X reduction in power consumption.

Researchers showed that performing computation directly on storage devices improves system performance in terms of energy consumption and processing time. For example, Riedel et al. [2] proposed an active disk which performs computation using the processor in a hard disk drive (HDD). Their experimental results showed that the active disk-based system had a factor of 2x performance improvement [2]. However, because the performance gap between the HDDs and CPUs becomes larger and larger, the active disk-based improvement is quite limited. As the role of flash memory increases in storage architectures, solid-state drives (SSDs) have gradually displaced the HDDs with higher access performance and lower power consumption. Researchers also proposed an active flash, which performs computation using a controller in the SSD [1]. However, the SSD controller needs to implement a flash translation layer to make the SSD as an emulated HDD for most operating systems. It also needs to communicate with a host interface to transfer required data. The additional computation power can be utilized is quite limited. To maximize the computation power on the SSD, we propose a processor design called storage processing unit (SPU).