Cross-layer Modeling and Design of Content Addressable Memories in Advanced Technology Nodes for Similarity Search
Siri Narla, Piyush Kumar, Mohammad Adnaan, Azad Naeemi
TL;DR
This work tackles how interconnect parasitics at the 7 nm node degrade CAM-based similarity search across SRAM-, SOT-MRAM-, and FeFET-based CAMs. It builds a cross-layer modeling flow that extracts device- and layout-level parasitics with ASAP7/nxtgrd, converts them into SPICE models, and maps Hamming distance ($HDist$) to matchline discharge delays, revealing that the delay difference between consecutive $HDist$ values scales as $t_n - t_{n-1} \propto 1/(n(n-1))$ and is sensitive to IR/RC effects. It proposes two mitigations— relocating search lines to upper metal levels (S2x) and RC-delay matching between clock and search paths— and demonstrates application-level gains on dataset search and a sequential recommender, while highlighting density and energy trade-offs for SOT- and FeFET-CAMs versus SRAM-CAM. The study provides a practical cross-layer framework and actionable guidance for deploying CAM-based similarity search in advanced nodes, illustrating when NVM-CAMs offer density benefits with favorable energy profiles.
Abstract
In this paper we present a comprehensive design and benchmarking study of Content Addressable Memory (CAM) at the 7nm technology node in the context of similarity search applications. We design CAM cells based on SRAM, spin-orbit torque, and ferroelectric field effect transistor devices and from their layouts extract cell parasitics using state of the art EDA tools. These parasitics are used to develop SPICE netlists to model search operations. We use a CAM-based dataset search and a sequential recommendation system to highlight the application-level performance degradation due to interconnect parasitics. We propose and evaluate two solutions to mitigate interconnect effects.
