TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers

This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed quantized network architecture with 422k parameters, enables real-time object detection on embedded microcontrollers, and it has been evaluated to exploit CNN accelerators. In particular, the proposed network has been deployed on the MAX78000 microcontroller achieving high frame-rate of up to 180fps and an ultra-low energy consumption of only 196{\mu}J per inference with an inference efficiency of more than 106 MAC/Cycle. TinyissimoYOLO can be trained for any multi-object detection. However, considering the small network size, adding object detection classes will increase the size and memory consumption of the network, thus object detection with up to 3 classes is demonstrated. Furthermore, the network is trained using quantization-aware training and deployed with 8-bit quantization on different microcontrollers, such as STM32H7A3, STM32L4R9, Apollo4b and on the MAX78000's CNN accelerator. Performance evaluations are presented in this paper

Plotinus: The First Philosopher of the Unconscious

Plotinus is sometimes referred to as “the first philosopher of the unconscious.” In his 1960 essay “Consciousness and Unconsciousness in Plotinus,” Hans Rudolph Schwyzer called Plotinus “the discoverer of the unconscious.” What exactly was Plotinus’ unconscious? In the Enneads, Plotinus asks about soul and intellect: “Why then…do we not consciously grasp them…? For not everything which is in the soul is immediately perceptible” (V.1.12.1–15).[i] In the De anima of Aristotle, “Mind does not think intermittently” (430a10–25).[ii]We cannot remember eternal mind in us, because passive mind is perishable. Is the productive or active intelligence in our mind that of which we are not conscious? Can productive intelligence be compared to unconscious thought? Plotinus suggests that we do not notice the activity of intellect because it is not engaged with objects of sense perception. The intellect must involve an activity prior to awareness. Awareness of intellectual activity only occurs when thinking is reflected as in a mirror, but knowledge in discursive reason, reason transitioning from one object to the next in a temporal sequence, is not self-knowledge. Only in the activity of intellect inaccessible to discursive reason is thinking as the equivalent of being. The intellectual act in mind is only apprehended when it is brought into the image-making power of mind through the logos or linguistic articulation; “we are always intellectually active but do not always apprehend our activity” (IV.3.30.1–17). If the Intellectual is the unconscious, then unconscious reason is superior to conscious reason. The inability of conscious reason to know itself in the illusion of self-consciousness is the premise of psychoanalysis in the twentieth century

ENERGY-EFFICIENT LIGHTWEIGHT ALGORITHMS FOR EMBEDDED SMART CAMERAS: DESIGN, IMPLEMENTATION AND PERFORMANCE ANALYSIS

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. Hence, computer vision algorithms running on these camera boards should be light-weight and efficient. This thesis is about designing and developing computer vision algorithms, which are aware and successfully overcome the limitations of embedded platforms (in terms of power consumption and memory usage). Particularly, we are interested in object detection and tracking methodologies and the impact of them on the performance and battery life of the CITRIC camera (embedded smart camera employed in this research). This thesis aims to prolong the life time of the Embedded Smart platform, without affecting the reliability of the system during surveillance tasks. Therefore, the reader is walked through the whole designing process, from the development and simulation, followed by the implementation and optimization, to the testing and performance analysis. The work presented in this thesis carries out not only software optimization, but also hardware-level operations during the stages of object detection and tracking. The performance of the algorithms introduced in this thesis are comparable to state-of-the-art object detection and tracking methods, such as Mixture of Gaussians, Eigen segmentation, color and coordinate tracking. Unlike the traditional methods, the newly-designed algorithms present notable reduction of the memory requirements, as well as the reduction of memory accesses per pixel. To accomplish the proposed goals, this work attempts to interconnect different levels of the embedded system architecture to make the platform more efficient in terms of energy and resource savings. Thus, the algorithms proposed are optimized at the API, middleware, and hardware levels to access the pixel information of the CMOS sensor directly. Only the required pixels are acquired in order to reduce the unnecessary communications overhead. Experimental results show that when exploiting the architecture capabilities of an embedded platform, 41.24% decrease in energy consumption, and 107.2% increase in battery-life can be accomplished. Compared to traditional object detection and tracking methods, the proposed work provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours