# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 1 Introduction  
With the advantages of transparency, softness, biocompatibility, etc., flexible electronic devices have been widely used in displaying and sensing fields [1]. For example, the flexible electronic devices have put the curved screen [2] and the electronic skin [3] into practice. Meanwhile, state of the art has done a lot of proven researches in the flexible self-energizing (solar, etc.) [4] and flexible antennas [5]. In some applications, like heart care, where biocompatibility is extremely vital, realizing a fully flexible system that including flexible sensing, computation, and display are necessary for its biocompatible physical properties. As shown in Figure 1, the innovative fully flexible system architecture, where the works on various types of flexible interfaces toward bio-tissue [3], flexible analog-to-digital converter(ADC) [6], memory [7], sensors, and display [1; 2] have been reported, however, the computation based on flexible devices is still an unsolved problem for the fully flexible system, especially for the complex algorithms with real-time processing requirements. The lower carrier mobility of the devices themselves and device deviation that is caused by immature manufactory make it impossible to implement the high-frequency and high-resolution digital processing system on flexible technology. Besides, high-speed, high-resolution flexible ADC and Digital Signal Processing (DSP) are needed if the traditional digital processing flow would be adopted, which is also energy hungry. In order to make up for the shortcomings of flexible computing, [PERSON] [8] has tried to combine the flexible devices with the silicon-based devices, where the complex computation part is done by silicon-based integrated circuits. Nevertheless, this kind of pseudo-fully-flexible system brings a lot of problems inevitably. Silicon is too hard to be applied in the biocompatible application and the interfaces between two devices are costly due to the totally different manufacturing process. [PERSON] [9] proposes an 8-bit general-purpose microprocessor that is based on thin-film transistor (TFT) devices, which could only perform simple algorithm, such as logic, arithmetic, and bit shift functions at a maximum frequency of 2.1 kHz. To sum up, the performance of TFT based digital circuit is not comparable with conventional silicon-based technology and is difficult to support the computation intensive real-time applications, such as the prevailing image signal processing and machine learning. So could we realize the fully flexible system by real-time computing with thin-film transistors? Analog circuits are well known as more energy-efficient and having a higher computing speed than their digital counterparts, for the clock-free processing mode and lower resource consumption. Analog accelerators for signal processing also have recently achieved the breakthrough in various fields [10], but analog TFT circuit is rarely reported due to the device deviation and component mismatch, which are also the major disadvantage of the flexible device. However, in some fault-tolerant applications, the deviation may not be critical. For example, in feature extraction algorithms of image processing, such as Difference-of-Gaussian (DoG) [11] and Histogram of Oriented Gradient (HOG) [12], the feature descriptors are generated from local or global statistical information, in which the exact original data does not make much sense. Moreover, in machine learning algorithms, such as multi-layer perception (MLP) or convolutional neural network (CNN), the mismatch, and as such other distortions will be absorbed in the trained classifier [13]. We proposed the analog accelerator for image pre-processing that based on the fully flexible system [14; 15]. Analog-to-Information processing (AIP) architecture was introduced to implement the typical neural network MLP [16]. We extended this paper to a fully flexible processing system that includes fault-tolerant pro-processing and computation-intensive processing for image classification.