Neural Networks, Edge Computing or Offloading — A study about how offloading neural network calculations stands in contrast to edge computing for embedded hardware
| dc.contributor.author | Frennborn, Erik | |
| dc.contributor.author | Oliv, Adam | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | Pathan, Risat | |
| dc.contributor.supervisor | Petersen Moura Trancoso, Pedro | |
| dc.date.accessioned | 2022-06-15T08:15:54Z | |
| dc.date.available | 2022-06-15T08:15:54Z | |
| dc.date.issued | 2022 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | The use of machine learning and neural networks shows no signs of slowing down in the embedded sector, but since embedded hardware often face performance or energy constraints that heavily limit the computational capacity of applications, it might not always be optimal to perform computations locally on the device. This study compares on-chip computing of neural networks on the embedded hardware i.MX 8M Plus with offloading the computation to a remote server supplied with Intel i9-10850K and a GeForce RTX 3070. This study also investigates to what extent the common compression techniques quantization, pruning and weight clustering affect performance metrics such as latency and energy consumption for embedded hardware. After measuring latency and energy consumption for non-pipelined inferences for 33 network variations we have discovered both a latency and energy consumption threshold where it is more effective to offload rather then computing on the edge device. These thresholds exist since latency for the offloading scenario remains almost constant. The latency threshold of 0,031 s is obtained from offloading via Ethernet to remote GPU, and is strongly limited by the network latency. This inference time could be used as a guideline for development meaning that if the inference time on embedded device exceeds set time, it is probably more efficient to offload calculations to remote server. Other discoveries points towards the conclusion that the NPU of the i.MX 8M Plus heavily favors compressed models, showing an average speedup of 188x on the NPU when models are compressed using quantization and pruning. | sv |
| dc.identifier.coursecode | DATX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/304696 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Offloading | sv |
| dc.subject | Convolutional neural networks (CNNs) | sv |
| dc.subject | Deep neural networks (DNNs) | sv |
| dc.subject | Embedded systems | sv |
| dc.subject | Edge computing | sv |
| dc.subject | Energy reduction | sv |
| dc.subject | Optimization | sv |
| dc.subject | Compression | sv |
| dc.title | Neural Networks, Edge Computing or Offloading — A study about how offloading neural network calculations stands in contrast to edge computing for embedded hardware | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |