2019/03/31 by Siqi Wang, Gayathri Ananthanarayanan, Yifan Zeng +3 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #Advanced Neural Network Applications #Algorithm #Artificial intelligence #Artificial neural network #CCD and CMOS Imaging Sensors #Computation #Computer engineering #Computer science #Convolution (computer science) #Convolutional neural network #Edge device #Enhanced Data Rates for GSM Evolution #Exploit #Inference #Multi-core processor #Overhead (engineering) #Parallel computing #Pipeline (software) #Pooling #Throughput #Wireless #cs.DC #cs.LG #cs.PF
paper · pdf · doi:10.1109/tcad.2019.2944584
published as in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 10, pp. 2254-2267, Oct. 2020 · Accepted to IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
openalex publication_date 2019/10/01 · arxiv created 2020/01/22 · arxiv updated 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Internet of Things edge intelligence requires convolutional neural network (CNN) inference to take place in the edge devices itself. ARM big.LITTLE architecture is at the heart of prevalent commercial edge devices. It comprises of single-ISA heterogeneous cores grouped into multiple homogeneous clusters that enable power and performance tradeoffs. All cores are expected to be simultaneously employed in inference to attain maximal throughput. However, high communication overhead involved in parallelization of computations from convolution kernels across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs pipelined design to split convolutional layers across clusters while limiting parallelization of their respective kernels to the assigned cluster. We develop a performance-prediction model that utilizes only the convolutional layer descriptors to predict the execution time of each layer individually on all permitted core configurations (type and count). Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in a 39% higher throughput than the highest antecedent throughput.