GEM3D CIM General Purpose Matrix Computation Using 3D Integrated SRAM eDRAM Hybrid Compute In Memory on Memory Architecture
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914475879170048 |
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| author | Chakraborty, Subhradip Singh, Ankur Jaiswal, Akhilesh R. |
| author_facet | Chakraborty, Subhradip Singh, Ankur Jaiswal, Akhilesh R. |
| contents | With the rapid growth of deep neural networks (DNNs), compute-in-memory (CIM) has emerged as a promising energy-efficient paradigm for accelerating multiply-and-accumulate (MAC) operations. Yet, current CIM architectures are largely limited to dot-product computations and struggle to efficiently support general-purpose matrix operations, such as transpose, element-wise addition, and multiplication. This work presents a 3D-integrated, memory-on-memory SRAM-eDRAM hybrid CIM architecture, implemented in GlobalFoundries 22~nm FDSOI technology, capable of performing general matrix operations directly within the memory crossbar with 4-bit precision. By leveraging a specialized transpose-based architecture, in-memory arithmetic operations, peripheral-aware design, and 3D SRAM--eDRAM integration, the proposed architecture balances latency, energy efficiency, and compute density for general purpose matrix operations while remaining compatible with the conventional CIM dot product architectures. Overall, this memory-on-memory CIM framework generalizes CIM beyond dot products, enabling versatile matrix processing and paving the way for broader applications in AI acceleration and general-purpose high performance computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13969 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GEM3D CIM General Purpose Matrix Computation Using 3D Integrated SRAM eDRAM Hybrid Compute In Memory on Memory Architecture Chakraborty, Subhradip Singh, Ankur Jaiswal, Akhilesh R. Hardware Architecture With the rapid growth of deep neural networks (DNNs), compute-in-memory (CIM) has emerged as a promising energy-efficient paradigm for accelerating multiply-and-accumulate (MAC) operations. Yet, current CIM architectures are largely limited to dot-product computations and struggle to efficiently support general-purpose matrix operations, such as transpose, element-wise addition, and multiplication. This work presents a 3D-integrated, memory-on-memory SRAM-eDRAM hybrid CIM architecture, implemented in GlobalFoundries 22~nm FDSOI technology, capable of performing general matrix operations directly within the memory crossbar with 4-bit precision. By leveraging a specialized transpose-based architecture, in-memory arithmetic operations, peripheral-aware design, and 3D SRAM--eDRAM integration, the proposed architecture balances latency, energy efficiency, and compute density for general purpose matrix operations while remaining compatible with the conventional CIM dot product architectures. Overall, this memory-on-memory CIM framework generalizes CIM beyond dot products, enabling versatile matrix processing and paving the way for broader applications in AI acceleration and general-purpose high performance computing. |
| title | GEM3D CIM General Purpose Matrix Computation Using 3D Integrated SRAM eDRAM Hybrid Compute In Memory on Memory Architecture |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2604.13969 |