Piergiulio Mannocci
Papers
1
Total Citations
2
H-Index
1
About
Piergiulio Mannocci is a leading researcher in the field of in-memory computing and analogue hardware acceleration, with a focus on energy-efficient architectures for artificial intelligence. His most-cited work, "A fully integrated analogue closed-loop in-memory computing accelerator based on static random-access memory" (2026), introduces a novel approach that leverages SRAM arrays to perform analogue computations directly within memory, eliminating the energy and latency costs of data movement. This design achieves high precision and stability through a closed-loop feedback mechanism, marking a significant advance in the practical deployment of analogue AI accelerators. With 2 citations in its early publication stage, the paper has already attracted attention from the hardware and machine learning communities. Mannocci’s contributions are particularly notable for bridging the gap between theoretical analogue computing and scalable, integrated circuit implementations. His work is foundational for next-generation edge AI devices, where low power consumption and real-time processing are critical. As a researcher, Mannocci continues to push the boundaries of non-von Neumann architectures, making him a key figure to watch in the evolution of efficient, brain-inspired computing.
Research Focus
Key Achievements
Top Papers
- 1