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NeuroPIM :
NeuroPIM : Felxible Neural Accelerator for Processing-in-Memory Architectures
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- Bidgoli, Ali M. (författare)
- University of Tehran, Iran
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- Fattahi, Sepideh (författare)
- University of Tehran, Iran
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- Rezaei, Seyyed H. S. (författare)
- University of Tehran, Iran
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- Modarressi, Mehdi (författare)
- University of Tehran, Iran; Institute for Research in Fundamental Sciences (IPM), School of Computer Science, Iran
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- Daneshtalab, Masoud (författare)
- Mälardalens universitet,Inbyggda system,Tallinn University of Technology, Tallinn, Estonia
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2023
- 2023
- Engelska.
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Ingår i: Proceedings - 2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems, DDECS 2023. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350332773 ; , s. 51-56
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The performance of microprocessors under many modern workloads is mainly limited by the off-chip memory bandwidth. The emerging process-in-memory paradigm present a unique opportunity to reduce data movement overheads by moving computation closer to memory. State-of-the-art processing-in-memory proposals stack a logic layer on top of one or multiple memory layers in a 3D fashion and leverage the logic layer to build near-memory processing units. Such processing units are either application-specific accelerators or general-purpose cores. In this paper, we present NeuroPIM, a new processing-in-memory architecture that uses a neural network as the memory-side general-purpose accelerator. This design is mainly motivated by the observation that in many real-world applications, some program regions, or even the entire program, can be replaced by a neural network that is learned to approximate the program's output. NeuroPIM benefits from both the flexibility of general-purpose processors and superior performance of application-specific accelerators. Experimental results show that NeuroPIM provides up to 41% speedup over a processor-side neural network accelerator and up to 8x speedup over a general-purpose processor.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Hardware acceleration
- Neural network
- Processing-in-memory
- Application programs
- Computation theory
- Computer circuits
- General purpose computers
- Network architecture
- Neural networks
- Program processors
- Application specific
- General purpose processors
- Logic layers
- Memory bandwidths
- Neural-networks
- Off-chip memory
- Performance
- Processing units
- Memory architecture
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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