In-memory processing

Résumé

In computer science, in-memory processing (PIM) is a computer architecture for processing data stored in an in-memory database. In-memory processing improves the power usage and performance of moving data between the processor and the main memory. Older systems have been based on disk storage and relational databases using Structured Query Language, which are increasingly regarded as inadequate to meet business intelligence (BI) needs. Because stored data is accessed much more quickly when it is placed in random-access memory (RAM) or flash memory, in-memory processing allows data to be analyzed in real time, enabling faster reporting and decision-making in business. Disk-based business intelligence Data structures With disk-based technology, data is loaded on to the computer's hard disk in the form of multiple tables and multi-dimensional structures against which queries are run. Disk-based technologies are relational database management systems (RDBMS), often b

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https://en.wikipedia.org/wiki/In-memory_processing

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Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution speedup only when disk accesses are significantly slower than in-memory query processing. On the other hand, fast storage offers loading times that approach or even outperform fully in-memory query execution response times, rendering purely frequency-based statistics incapable of capturing impact of a caching decision on query execution. For example, caching the input of a frequent query that spends most of its time processing joins is less beneficial than caching a page for a slightly less frequent but scan-heavy query. As a result, existing caching policies waste valuable memory space to cache input data that offer little-to-no acceleration for analytics. This paper proposes HPCache, a buffer management policy that enables fast analytics on high-bandwidth storage by efficiently using the available in-memory space. HPCache caches data based on their speedup potential instead of relying on frequency-based statistics. We show that, with fast storage, the benefit of in-memory caching varies significantly across queries; therefore, we quantify the efficiency of caching decisions and formulate an optimization problem. We implement HPCache in Proteus and show that i) estimating speedup potential improves memory space utilization, and ii) simple runtime statistics suffice to infer speedup expectations. We show that HPCache achieves up to 12% faster query execution over state-of-the-art caching policies, or 75% less in-memory cache footprint without deteriorating query performance. Overall, HPCache enables efficient use of the in-memory space for input caching in the presence of fast storage, without any requirement for workload predictions.

ACM2022

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Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices,

IEEE2019

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Non-volatile analog memory devices such as phase-change memory (PCM) enable designing dedicated connectivity matrices for the hardware implementation of deep neural networks (DNN). In this in-memory computing approach, the analog conductance states of the memory device can be gradually updated to train DNNs on-chip or software trained connection strengths may be programmed one-time to the devices to create efficient inference engines. Reliable and computationally simple models that capture the non-ideal programming and temporal evolution of the devices are needed for evaluating the training and inference performance of the deep learning hardware based on in-memory computing. In this paper, we present statistically accurate models for PCM, based on the characterization of more than 10,000 devices, that capture the state-dependent nature and variability of the conductance update, conductance drift, and read noise. Integrating the computationally simple device models with deep learning frameworks such as TensorFlow enables us to realistically evaluate training and inference performance of the PCM array based hardware implementations of DNNs.

IEEE2019

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