Abstract
GIRAF is an in-storage architecture and algorithm framework based on Resistive Content Addressable Memory (RCAM). GIRAF functions simultaneously as a storage and a massively parallel associative processor. GIRAF alleviates the bandwidth wall by connecting every memory bit to processing transistors and keeping computing inside the storage arrays, thus implementing in-data, rather than near-data, processing. We show that GIRAF outperforms a reference computer architecture with a bandwidth-limited external storage access on a variety of data intensive workloads. The performance of GIRAF Euclidean distance, dot product and histogram implementation, exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size. The performance of GIRAF SpMV exceeds the attainable performance of such reference architecture by more than two orders of magnitude.
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