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This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highlighting their collective potential for low-power edge computing enviro
Externí odkaz:
http://arxiv.org/abs/2408.12767
Due to the high computation overhead of Vision Transformers (ViTs), In-memory Computing architectures are being researched towards energy-efficient deployment in edge-computing scenarios. Prior works have proposed efficient algorithm-hardware co-desi
Externí odkaz:
http://arxiv.org/abs/2408.12742
Autor:
Chellu, Abhiroop, Bej, Subhajit, Wahl, Hanna, Kahle, Hermann, Uusitalo, Topi, Hytönen, Roosa, Rekola, Heikki, Lang, Jouko, Schöll, Eva, Hanschke, Lukas, Kallert, Patricia, Kipp, Tobias, Strelow, Christian, Tuominen, Marjukka, Jöns, Klaus D., Karvinen, Petri, Niemi, Tapio, Guina, Mircea, Hakkarainen, Teemu
On-chip emitters that generate single and entangled photons are essential for photonic quantum information processing technologies. Semiconductor quantum dots (QDs) are attractive candidates that emit high-quality quantum states of light, however at
Externí odkaz:
http://arxiv.org/abs/2407.11642
Unsupervised pre-training has emerged as a transformative paradigm, displaying remarkable advancements in various domains. However, the susceptibility to domain shift, where pre-training data distribution differs from fine-tuning, poses a significant
Externí odkaz:
http://arxiv.org/abs/2405.12781
The attention module in vision transformers(ViTs) performs intricate spatial correlations, contributing significantly to accuracy and delay. It is thereby important to modulate the number of attentions according to the input feature complexity for op
Externí odkaz:
http://arxiv.org/abs/2404.15185
Autor:
Hakkarainen, Teemu, Hilska, Joonas, Hietalahti, Arttu, Ranta, Sanna, Peil, Markus, Kantola, Emmi, Chellu, Abhiroop, Sen, Efsane, Penttinen, Jussi-Pekka, Guina, Mircea
Deterministic light sources capable of generating quantum states on-demand at wavelengths compatible with fiber optics and atmospheric transmission are essential for practical applications in quantum communication, photonic quantum computing, and qua
Externí odkaz:
http://arxiv.org/abs/2404.06083
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024
Transformers have revolutionized various real-world applications from natural language processing to computer vision. However, traditional von-Neumann computing paradigm faces memory and bandwidth limitations in accelerating transformers owing to the
Externí odkaz:
http://arxiv.org/abs/2402.02586
AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies have explored replacing SRAM with
Externí odkaz:
http://arxiv.org/abs/2312.03559
Autor:
Agarwal, Arpit, Ajith, Abhiroop, Wen, Chengtao, Stryzheus, Veniamin, Miller, Brian, Chen, Matthew, Johnson, Micah K., Rincon, Jose Luis Susa, Rosca, Justinian, Yuan, Wenzhen
In manufacturing processes, surface inspection is a key requirement for quality assessment and damage localization. Due to this, automated surface anomaly detection has become a promising area of research in various industrial inspection systems. A p
Externí odkaz:
http://arxiv.org/abs/2309.04590
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data representations. While recent SNN algorithmic advances achieve hig
Externí odkaz:
http://arxiv.org/abs/2309.03388