A technical paper titled “Cross-layer Modeling and Design of Content Addressable Memories in Advanced Technology Nodes for Similarity Search” was published by researchers at the Georgia Institute of Technology.
“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.”
Find the technical paper here. Published March 2024.
Narla, Siri, Piyush Kumar, Mohammad Adnaan, and Azad Naeemi. “Cross-layer Modeling and Design of Content Addressable Memories in Advanced Technology Nodes for Similarity Search.” arXiv preprint arXiv:2403.15328 (2024).
Further Reading
SRAM Scaling Issues, And What Comes Next
While it will remain a workhorse memory, using SRAM at advanced nodes requires new approaches.
SRAM In AI: The Future Of Memory
Why SRAM is viewed as a critical element in new and traditional compute architectures.
MRAM Getting More Attention At Smallest Nodes
Why this 25-year-old technology may be the memory of choice for leading edge designs and in automotive applications.
ReRAM Seeks To Replace NOR
There is increased interest in ReRAM for embedded computing, especially in automotive applications, as more of its known issues are solved. Nevertheless, there is no one-size-fits-all NVM.
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