2026
conference-paper
OpenAlex
Konstantinos Stavrakakis, Bas Smeele, Emmanouil Arapidis, Theofilos Spyrou et autres
Computing-in-Memory (CIM) architectures using memristive crossbar arrays enable energy-efficient AI acceleration. Analog non-idealities, such as IR drop and nonlinearity, impose design constraints that existing simulators cannot capture and thus explore effectively. Current approaches sacrifice either modeling accuracy or simulation speed, preventing systematic …
nl
(code pays fourni par la source)
2026
conference-paper
OpenAlex
Rajendra Bishnoi, Mohammad Amin Yaldagard, Konstantinos Stavrakakis, Said Hamdioui et autres
The objective of our collaborative multi-partner project is to create an open-source Deep Learning framework called AIDGE for edge and embedded Artificial Intelligence (AI), built around an established European value chain. The framework is designed to support diverse application domains that function …
nl, at, au, de, fr
(code pays fourni par la source)
2026
conference-paper
OpenAlex
Theofilos Spyrou, Yashvardhan Biyani, Konstantinos Stavrakakis, Rajendra Bishnoi et autres
The Computing-In-Memory (CIM) paradigm offers a promising solution to the memory-wall bottleneck that limits conventional Von Neumann architectures. By performing data processing at the same physical location where the data are stored, CIM-based architectures minimize costly data movement and drastically improve energy …
nl, fr, de, cz, it, ch
(code pays fourni par la source)
2026
conference-paper
OpenAlex
Christian Larmann, Abdullah Aljuffri, Adrian Marotzke, Alejandro Garza et autres
This paper presents a Physical Memory Protection Snapshot Engine (PSE), a lightweight hardware extension for RISC-V that addresses both performance and security challenges of Physical Memory Protection (PMP) reconfiguration. By storing and restoring full PMP configurations in a single cycle, the PSE …
nl, sa, de
(code pays fourni par la source)
2026
conference-paper
OpenAlex
Emmanouil Arapidis, Theofilos Spyrou, Konstantinos Stavrakakis, Emmanouil Anastasios Serlis et autres
Although offering great potential for energy-efficient edge-AI, memristor-based CIM accelerators are severely hindered by IR drop induced errors. To tackle this, we propose a low-cost mitigation technique by first quantifying the impact of IR drop on the accuracy. Then, a mitigation strategy …
nl
(code pays fourni par la source)