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The large amount of memory usage in recent machine learning applications imposes a significant system burden with respect to power and processing speed. To cope with such a problem, Processing-In-Memory (PIM) techniques can be applied as an alternative solution. Especially, the recommendation system, which is one of the major machine learning applications used in data centers, requires a large memory capacity and therefore represents a suitable candidate application that could be helped by the PIM technique. In this paper, we introduce a machine learning framework, PIMCaffe, designed for in-memory neural processing units and its evaluation environment. PIMCaffe consists of two components: a Caffe2-based deep learning framework that supports PIM acceleration and a PIM-emulating hardware platform. We develop a suite of functions, libraries, application programming interfaces, and a device driver to support the framework. In addition, we implement a prototype Neural Processing Unit (NPU) in PIMCaffe to evaluate the performance of our platform with machine learning applications. Our prototype NPU design includes a vector processor for parallel vector processing and a systolic array unit for matrix multiplication. Using the proposed software framework, we perform a detailed analysis of the in-memory neural processing unit. PIMCaffe supports evaluations of recommendation systems and various convolutional neural network models on the in-memory neural processing unit. PIMCaffe with the NPU shows up to <inline-formula> <tex-math notation="LaTeX">$2.26\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5.99\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.71\times $ </tex-math></inline-formula> speedup, compared to the ARM Cortex-A53 CPU, for the recommendation system, AlexNet, and ResNet-50, respectively.

PIMCaffe: Functional Evaluation of a Machine Learning Framework for In-Memory Neural Processing Unit