PIMCaffe: Functional Evaluation of a Machine Learning Framework for In-Memory Neural Processing Unit
| Autor: | Dong-seok Kang, Hongju Kal, Won Jeon, Ji Won Lee, Won Woo Ro |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Speedup
General Computer Science Computer science Machine learning computer.software_genre FPGA prototyping Convolutional neural network recommendation system Deep learning framework General Materials Science functionality Artificial neural network business.industry Deep learning General Engineering Vector processor TK1-9971 Software framework neural processing unit Memory management Central processing unit Artificial intelligence Electrical engineering. Electronics. Nuclear engineering business computer processing-in-memory |
| Zdroj: | IEEE Access, Vol 9, Pp 96629-96640 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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 $2.26\times $ , $5.99\times $ , and $1.71\times $ speedup, compared to the ARM Cortex-A53 CPU, for the recommendation system, AlexNet, and ResNet-50, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|