Recent advancements in deep learning techniques facilitate intelligent-query support in diverse applications, such as content-based image retrieval and audio texturing. Unlike conventional key-based queries, these intelligent queries lack efficient indexing and require complex compute operations for feature matching. To achieve high-performance intelligent querying against massive datasets, modern computing systems employ GPUs in-conjunction with solid-state drives (SSDs) for fast data access and parallel data processing. However, our characterization with various intelligent-query workloads developed with deep neural networks (DNNs), shows that the storage I/O bandwidth is still the major bottleneck that contributes 56%--90% of the query execution time. To this end, we present DeepStore, an in-storage accelerator architecture for intelligent queries. It consists of (1) energy-efficient in-storage accelerators designed specifically for supporting DNN-based intelligent queries, under the resource constraints in modern SSD controllers; (2) a similarity-based in-storage query cache to exploit the temporal locality of user queries for further performance improvement; and (3) a lightweight in-storage runtime system working as the query engine, which provides a simple software abstraction to support different types of intelligent queries. DeepStore exploits SSD parallelisms with design space exploration for achieving the maximal energy efficiency for in-storage accelerators. We validate DeepStore design with an SSD simulator, andmore »
Dual-storage-port Nonvolatile SRAM based on Back-end-of-the-line Processed Hf0.5Zr0.5O2 Ferroelectric Capacitors Towards 3D Selector-free Cross-point Memory
This work presents the design and experimental demonstration of a novel dual-storage-portnonvolatile SRAM based on back-end-of-the-line processed Hf0.5Zr0.5O2-based metal-ferroelectric-metalcapacitors, which offers significant advantages over the conventional single-storage-port version withoutarea penalty, and paves the way for implementing our proposed selector-free 3D cross-point memory.
- Liu, M.
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- IEEE transactions on electron devices
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- National Science Foundation
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