title: 24-GOPS 4.5- mm² digital cellular neural network for rapid visual attention in an object-recognition SoC.
abstract: This paper presents the Visual Attention Engine (VAE), which is a digital cellular neural network (CNN) that executes the VA algorithm to speed up object-recognition. The proposed time-multiplexed processing element (TMPE) CNN topology achieves high performance and small area by integrating 4800 (80 × 60) cells and 120 PEs. Pipelined operation of the PEs and single-cycle global shift capability of the cells result in a high PE utilization ratio of 93%. The cells are implemented by 6T static random access memory-based register files and dynamic shift registers to enable a small area of 4.5 mm(2). The bus connections between PEs and cells are optimized to minimize power consumption. The VAE is integrated within an object-recognition system-on-chip (SoC) fabricated in the 0.13- μm complementary metal-oxide-semiconductor process. It achieves 24 GOPS peak performance and 22 GOPS sustained performance at 200 MHz enabling one CNN iteration on an 80 × 60 pixel image to be completed in just 4.3 μs. With VA enabled using the VAE, the workload of the object-recognition SoC is significantly reduced, resulting in 83% higher frame rate while consuming 45% less energy per frame without degradation of recognition accuracy.
authors.affiliation: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea. seungjin@eeinfo.kaist.ac.kr
authors.forename: Seungjin
authors.initials: S
authors.lastname: Lee
authors.forename: Minsu
authors.initials: M
authors.lastname: Kim
authors.forename: Kwanho
authors.initials: K
authors.lastname: Kim
authors.forename: Joo-Young
authors.initials: JY
authors.lastname: Kim
authors.forename: Hoi-Jun
authors.initials: HJ
authors.lastname: Yoo
mesh_terms: D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000592:standards*; D010364:Pattern Recognition, Visual / Q000502:physiology*