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.

翻译：为应对人工智能工作负载激增的计算需求，神经形态架构通过模拟生物神经网络，被提出作为一种比传统冯·诺依曼架构更高效的替代方案。近期关于智能的神经科学理论表明，皮层柱是大脑新皮层的基本计算单元，智能源于皮层柱通过结构化参考框架进行信息存储、预测和推理的能力。基于该理论，近期研究已通过软件仿真实现了类脑视觉物体识别。本工作是首次尝试直接通过CMOS实现参考框架，以构建基于皮层柱的神经形态处理器。我们提出NeRTCAM（神经形态反向三态内容寻址存储器），这是一种基于CAM的构建模块，支持使用参考框架进行推理所需的关键操作（存储、预测、推理）。本文详细介绍了NeRTCAM架构及其关键组件。所有设计均采用SystemVerilog实现，并在7nm CMOS工艺下完成综合，同时评估了不同存储容量下的硬件复杂度扩展性。针对生物学启发的MNIST推理任务，采用1024条目存储容量的NeRTCAM系统仅占用0.15 mm²面积、消耗400 mW功率，关键路径延迟为9.18微秒，验证了基于CAM的参考框架直接CMOS实现的可行性。