NeuroPIM: Felxible Neural Accelerator for Processing-in-Memory Architectures

NeuroPIM : Felxible Neural Accelerator for Processing-in-Memory Architectures

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.

Fattahi, SepidehUniversity of Tehran, Iran (författare)
Rezaei, Seyyed H. S.University of Tehran, Iran (författare)
Modarressi, MehdiUniversity of Tehran, Iran; Institute for Research in Fundamental Sciences (IPM), School of Computer Science, Iran (författare)
Daneshtalab, MasoudMälardalens universitet,Inbyggda system,Tallinn University of Technology, Tallinn, Estonia(Swepub:mdh)mdb01 (författare)
University of Tehran, IranUniversity of Tehran, Iran; Institute for Research in Fundamental Sciences (IPM), School of Computer Science, Iran (creator_code:org_t)

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., s. 51-569798350332773

Av författaren/redakt...: Bidgoli, Ali M.; Fattahi, Sepideh; Rezaei, Seyyed H ...; Modarressi, Mehd ...; Daneshtalab, Mas ...

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