  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.
