Heterogeneous Computing in the Edge
INTRODUCTION
Heterogeneous computing is the technique where different types of processors with different data path architectures are applied together to optimize the execution of specific computational workloads . Traditional CPUs are often inefficient for the types of computational workloads we will run on edge computing nodes . By adding additional types of processing resources like GPUs , TPUs , and FPGAs , system operation can be optimized .
This technique is growing in popularity in cloud data centers , but is nascent in edge computing nodes . This paper will discuss some of the types of processors used in heterogenous computing , leading suppliers of these technologies , example edge use cases that benefit from each type , partitioning techniques to optimize its application , and hardware / software architectures to implement it in edge nodes .
Edge computing is a technique through which the computational , storage , and networking functions of an IoT network are distributed to a layer or layers of edge nodes arranged between the bottom of the cloud and the top of IoT devices 1 . There are many tradeoffs to consider when deciding how to partition workloads between cloud data centers and edge computing nodes , and which processor data path architecture ( s ) are optimum at each layer for different applications .
Figure 1 is an abstracted view of a cloud-edge network that employs heterogenous computing . A cloud data center hosts a number of types of computing resources , with a central interconnect . These computing resources consist of traditional Complex Instruction Set Computing / Reduced Instruction Set Computing ( CISC / RISC ) servers , but also include Graphics Processing Unit ( GPU ) accelerators , Tensor Processing Units ( TPUs ), and Field Programmable Gate Array ( FPGA ) farms and a few other processor types to help accelerate certain types of workloads .
Many of the capabilities of the cloud data center are mirrored in the heterogenous computing architecture of the edge computer node . It includes modules for multiple processor types , including CISC / RISC CPUs , GPUs , TPUs , and FPGAs . The compute workloads can not only be partitioned between edge and cloud ( see the companion article in this issue 2 ), but also partitioned between the various heterogenous processing resources on both levels .
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C . Byers , “ Key Criteria to Move Cloud Workloads to the Edge ,” IIC Journal of Innovation , June 2021
- 44 - June 2021