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)  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.
|Title of host publication||2016 IEEE International Solid-State Circuits Conference, ISSCC 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||2|
|State||Published - 23 Feb 2016|
|Event||63rd IEEE International Solid-State Circuits Conference, ISSCC 2016 - San Francisco, United States|
Duration: 31 Jan 2016 → 4 Feb 2016
|Name||Digest of Technical Papers - IEEE International Solid-State Circuits Conference|
|Conference||63rd IEEE International Solid-State Circuits Conference, ISSCC 2016|
|Period||31/01/16 → 4/02/16|
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