A Computing-in-Memory Engine for Searching on Homomorphically Encrypted Data

The high volumes of data stored in the cloud, coupled with growing concerns about security and privacy, have motivated research on homomorphic encryption (HE), i.e., a technique that enables computation directly on encrypted data, obviating the need for prior decryption. Recent algorithmic advances have enabled a diverse set of homomorphic operations (e.g., addition, multiplication, and division). Looking at the applications, recent work also suggests extensibility secure, homomorphically encrypted content-addressable memories [or secure content-addressable memories (SCAMs)]. Still, the large datawords that result from homomorphic data encodings (i.e., that must be stored/transferred for computation), compounded with the implicit computational complexity of HE, still impede the deployment of homomorphic computer hardware. As an alternative, computing-in-memory (CiM) architectures could significantly reduce the volume of data transfers for SCAM (and other) applications, leading to considerable energy savings and latency reduction. In this regard, we propose a CiM-compatible engine for SCAM (CiM-SCAM) and analyze the pros and cons of three different memory cells: a 6T CMOS SRAM and two memory cells based on ferroelectric field-effect transistors (FeFETs) (specifically 2T + 1 FeFET and 1-FeFET designs). CiM-SCAM leverages in-place copy buffers (IPCBs), along with customized sense amplifiers that include two types of in-memory adders. Our results suggest that energy (and search time) improvements of up to $16\times $ ( $3.2\times $ ) for 1-FeFET memory cells are possible, compared with an application-specific integrated circuit (ASIC) approach. Similar improvements are also possible with SRAM and 2T + 1-FeFET memory cells. For the latter, we achieve up to $13\times $ ( $3.1\times $ ) of energy (speedup).