We have designed a fully-integrated analog CMOS cognitive image sensor based on a two-layer artificial neural network and targeted to low-resolution image classification. We have used a single poly 180 nm CMOS process technology, which includes process modules for realizing the building blocks of the CMOS image sensor. Our design includes all the analog sub-circuits required to perform the cognitive sensing task, from image sensing to output classification decision. The weights of the network are stored in single-poly floating-gate memory cells, using a single transistor per analog weight. This enables the classifier to be intrinsically reconfigurable, and to be trained for various classification problems, based on low-resolution images. As a case study, the classifier capability is tested using a low-resolution version of the MNIST dataset of handwritten digits. The circuit exhibits a classification accuracy of 87.8%, that is comparable to an equivalent software implementation operating in the digital domain with floating point data precision, with an average energy consumption of 6 nJ per inference, a latency of 22.5 µs and a throughput of up to 133.3 thousand inferences per second. 13 has been launched in May 2020 [6]: the intelligent CIS has 38 been designed by equipping a conventional image sensor with 39 a digital signal processor (DSP) dedicated to AI processing 40 tasks and the memory for the AI model. 41 ANNs are composed of layers of artificial neurons. The 42 basic computation in the ANNs is the multiply-accumulate 43 (MAC) operation [7], [8], [9], that is the elementary operation 44 of a vector-matrix multiplication, where an input data vector 45 is multiplied by a matrix of fixed weights. As the size of the 46 ANN increases, the increased number of MAC operations, 47 as well as storage requirements and weight access opera-48 tions, result in a huge energy consumption in conventional 49 digital systems [10], [11], [12]. In order to reduce power 50 consumption per inference, thus enabling battery-powered 51 systems to be equipped with ANNs, a lot of research effort is 52 today being devoted to the design of analog ANN integrated 53 circuits [2], [13], [14], [15], [16], [17], [18], which exploit 54 basic properties of CMOS devices and circuits to allow a 55 very high degree of parallelism in MAC operations along with 56 in-memory computation. 57 Reduced precision of both input data and of processing 58 tasks, typical of the analog domain, has been demonstrated 59 to be well tolerated by neural networks [14]. For instance, 60 a fully integrated, on-chip ANN classifier architecture based 61 on analog circuits for low-resolution image classification 62 has been presented in [17]. There are many applications of 63 146 all quantities fall in their corresponding range. When con-177 sidering the conversion factors from software variables to 178 electrical signals, the full-scale ranges have been chosen to 179 comprise about 99% of the data of the distributions: [-4,4], 180 [-3,3], [-4,4] and [-20,20] were sele...