NeRTCAM: CAM-Based CMOS Implementation of Reference Frames for Neuromorphic Processors
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913356962594816 |
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| author | Nair, Harideep Leyman, William Sampath, Agastya Jacobson, Quinn Shen, John Paul |
| author_facet | Nair, Harideep Leyman, William Sampath, Agastya Jacobson, Quinn Shen, John Paul |
| contents | Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI workloads. Recent neuroscience theory on intelligence suggests that Cortical Columns (CCs) are the fundamental compute units in the neocortex and intelligence arises from CC's ability to store, predict and infer information via structured Reference Frames (RFs). Based on this theory, recent works have demonstrated brain-like visual object recognition using software simulation. Our work is the first attempt towards direct CMOS implementation of Reference Frames for building CC-based neuromorphic processors. We propose NeRTCAM (Neuromorphic Reverse Ternary Content Addressable Memory), a CAM-based building block that supports the key operations (store, predict, infer) required to perform inference using RFs. NeRTCAM architecture is presented in detail including its key components. All designs are implemented in SystemVerilog and synthesized in 7nm CMOS, and hardware complexity scaling is evaluated for varying storage sizes. NeRTCAM system for biologically motivated MNIST inference with a storage size of 1024 entries incurs just 0.15 mm^2 area, 400 mW power and 9.18 us critical path latency, demonstrating the feasibility of direct CMOS implementation of CAM-based Reference Frames. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeRTCAM: CAM-Based CMOS Implementation of Reference Frames for Neuromorphic Processors Nair, Harideep Leyman, William Sampath, Agastya Jacobson, Quinn Shen, John Paul Hardware Architecture Emerging Technologies Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI workloads. Recent neuroscience theory on intelligence suggests that Cortical Columns (CCs) are the fundamental compute units in the neocortex and intelligence arises from CC's ability to store, predict and infer information via structured Reference Frames (RFs). Based on this theory, recent works have demonstrated brain-like visual object recognition using software simulation. Our work is the first attempt towards direct CMOS implementation of Reference Frames for building CC-based neuromorphic processors. We propose NeRTCAM (Neuromorphic Reverse Ternary Content Addressable Memory), a CAM-based building block that supports the key operations (store, predict, infer) required to perform inference using RFs. NeRTCAM architecture is presented in detail including its key components. All designs are implemented in SystemVerilog and synthesized in 7nm CMOS, and hardware complexity scaling is evaluated for varying storage sizes. NeRTCAM system for biologically motivated MNIST inference with a storage size of 1024 entries incurs just 0.15 mm^2 area, 400 mW power and 9.18 us critical path latency, demonstrating the feasibility of direct CMOS implementation of CAM-based Reference Frames. |
| title | NeRTCAM: CAM-Based CMOS Implementation of Reference Frames for Neuromorphic Processors |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2405.11844 |