A 818-4094 TOPS/W Capacitor-Reconfigured Analog CIM for Unified Acceleration of CNNs and Transformers

The rapid evolution of machine learning has led to the emergence of diverse neural network architectures, such as CNNs, Transformers, and their hybrid models, each with unique computational precision requirements. Transformers, in particular, demand higher precision compared to CNNs. Existing analog...

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Bibliographic Details
Published in:IEEE journal of solid-state circuits 2025-05, Vol.60 (5), p.1844-1855
Main Author: Yoshioka, Kentaro
Format: Article
Language:English
Subjects:
Analog compute-in-memory (ACIM)
Artificial neural networks
capacitor reconfigured CIM (CR-CIM)
Capacitors
CNN
Co-design
Common Information Model (computing)
Computation
Computational efficiency
Computer architecture
Efficiency
In-memory computing
Machine learning
Microprocessors
Noise levels
Parallel processing
Power efficiency
Signal to noise ratio
transformer
Transformers
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