The typical way to learn programming is using different high-level programming languages and a simple text editor, in order to compose and compile programs, usually to solve simple computational problems. Nowadays, an alternative method for the introduction of lower secondary school students into programming is through educational robotics. In this work, we design, organize and implement a teaching model in the context of flipped classroom learning, using educational robotics and an asynchronous tele-education environment for courses with limited weekly school time. Such a model must include learning independent of time and place, a more efficient use of classroom time and a cooperative teaching technique in cross-curricular subjects. The whole process is evaluated by the lower secondary school students themselves through a data collection tool and the relevant results are presented.

Traditional processor architectures utilize an external DRAM for data storage, while they also operate under worst-case timing constraints. Such designs are heavily constrained by the delay costs of the data transfer between the core pipeline and the DRAM, and they are incapable of exploiting the timing variations of their pipeline stages. In this work, we focus on a near-data processing methodology combined with a novel timing analysis technique that enables the adaptive frequency scaling of the core clock and boosts the performance of low-power designs. We propose a near-data processing and better-than-worst-case co-design methodology to efficiently move the instruction execution to the DRAM side and, at the same time, to allow the pipeline to operate at higher clock frequencies compared to the worst-case approach. To this end, we develop a timing analysis technique, which evaluates the timing requirements of individual instructions and we dynamically scale the clock frequency, according to the instructions types that currently occupy the pipeline. We evaluate the proposed methodology on six different RISC-V post-layout implementations using an HMC DRAM to enable the processing-in-memory (PIM) process. Results indicate an average speedup factor of 1.96× with a 1.6× reduction in energy consumption compared to a standard RISC-V PIM baseline implementation.