Bi-i: a standalone ultra high speed cellular vision system

Abstract

FeatureThe Bi-i standalone cellular vision system is introduced and discussed. In the first part, the underlying sensor and system level architectures are presented and various implementations are overviewed. This computing platform consists of state-of-the-art sensing, cellular sensing-processing, digital signal processing and communication devices that make it possible to use the system as an ideal computing platform for combined topographic and non-topographic calculations in sensing-processing-actuation scenarios. In the second part of the paper, ultra-high frame rate laboratory experiments are shown and discussed to highlight the most peculiar features of the system and its applicability in various industrial quality control areas. The overview underlines the potentials of the Bi-i vision system for unmanned intelligent vehicle applications in visual exploration, identification, tracking and navigation.

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

“…In this regard, segmentation is essentially the first step toward many image analysis and computer vision problems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. With the recent advances in several new multimedia applications, there is the need to develop segmentation algorithms running on efficient hardware platforms [16][17][18].…”

“…This architecture offers great computational capabilities, which are suitable for complex image-analysis operations in objectoriented approaches [8][9][10]. Note that so far few CNN algorithms for obtaining the segmentation of a video sequence into moving objects have been introduced [5,6].…”

“…These segmentation algorithms were only simulated, i.e., the hardware implementation of these algorithms is substantially lacking. Based on these considerations, this article presents the implementation of a novel CNN-based segmentation algorithm onto the Bio-inspired (Bi-i) Cellular Vision System [9]. This system builds on CNN type (ACE16k) and DSP type (TX 6×) microprocessors [9].…”

“…Based on these considerations, this article presents the implementation of a novel CNN-based segmentation algorithm onto the Bio-inspired (Bi-i) Cellular Vision System [9]. This system builds on CNN type (ACE16k) and DSP type (TX 6×) microprocessors [9]. The proposed segmentation approach focuses on the algorithmic issues of the Bi-i platform, rather than on the architectural ones.…”

Stochastic bitstream‐based CNN and its implementation on FPGA

“…After the first successfully functioning analog CNN chips (Ace4k, Ace16k and CACE1, sized 64×64, 128×128 and 32×32 [8][9][10]) and systems (e.g. Bi-i and for SCAMP3 [11,12]) using these chips, the development and implementation of new chips and systems are in progress and can be reached on the market (EyeRis I and II [4]). Besides, the analog implementations attempts have been made to design fully digital versions [6,[13][14][15].…”