TY - GEN
T1 - A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems
AU - Sim, Jaehyeong
AU - Park, Jun Seok
AU - Kim, Minhye
AU - Bae, Dongmyung
AU - Choi, Yeongjae
AU - Kim, Lee Sup
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/2/23
Y1 - 2016/2/23
N2 - Transmitting massive amounts of image and audio data acquired by Internet-of-Everything (IoE) devices to data center servers for intelligent recognition processes is impractical for energy reasons, requiring in-situ processing of such data. However, algorithms accelerated by previous recognition processors [1, 2] are limited to specific applications, therefore, each IoE device may require an application-specific accelerator. On the other hand, deep convolutional neural networks (CNNs) [3] are a promising machine-learning approach, showing state-of-the-art recognition accuracy in a wide variety of applications, including both image and audio recognition. This makes CNNs a suitable candidate for a universal recognition platform for IoE devices, as described in Fig. 14.6.1. Due to the computational complexity and significant memory requirements of CNNs, a microcontroller unit (MCU) typically used for IoE devices is incapable of producing a meaningful recognition result in an energy-efficient way. Hence, the implementation of an energy-efficient CNN processor is desired to realize intelligent IoE systems.
AB - Transmitting massive amounts of image and audio data acquired by Internet-of-Everything (IoE) devices to data center servers for intelligent recognition processes is impractical for energy reasons, requiring in-situ processing of such data. However, algorithms accelerated by previous recognition processors [1, 2] are limited to specific applications, therefore, each IoE device may require an application-specific accelerator. On the other hand, deep convolutional neural networks (CNNs) [3] are a promising machine-learning approach, showing state-of-the-art recognition accuracy in a wide variety of applications, including both image and audio recognition. This makes CNNs a suitable candidate for a universal recognition platform for IoE devices, as described in Fig. 14.6.1. Due to the computational complexity and significant memory requirements of CNNs, a microcontroller unit (MCU) typically used for IoE devices is incapable of producing a meaningful recognition result in an energy-efficient way. Hence, the implementation of an energy-efficient CNN processor is desired to realize intelligent IoE systems.
UR - http://www.scopus.com/inward/record.url?scp=84962847015&partnerID=8YFLogxK
U2 - 10.1109/ISSCC.2016.7418008
DO - 10.1109/ISSCC.2016.7418008
M3 - Conference contribution
AN - SCOPUS:84962847015
T3 - Digest of Technical Papers - IEEE International Solid-State Circuits Conference
SP - 264
EP - 265
BT - 2016 IEEE International Solid-State Circuits Conference, ISSCC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 January 2016 through 4 February 2016
ER -