Digital self-timed neuron design for Spiking Neuron Networks

Spiking Neural Networks(SNN) have been widely leveraged by neuromorphic systems due to their ability to closely mimic biological neural behavior, where information is exchanged and received between neurons in the form of sparse events(spikes). Such neuromorphic systems are highly energy-efficient because the use of a global clock can be avoided...

Cryogenic Digital CMOS Memories for Quantum Computing

Scalable universal quantum computers require classical control hardware, physically close to the quantum devices at cryogenic temperatures. Such classical controllers need digital memory for various applications, ranging from high-speed queues to high-speed and low-speed lookup tables and working memory. The power consumption of the memories...

Firmware Updates Over The Air for LoRa using Random Linear Network Coding

LoRaWAN is a public Wireless Sensor network with excellent properties like being long-range, low-energy radios and resulting in long battery life. Devices are connected to this network through gateways, and they will run in that deployment for years without replacement. Therefore, bugs and security issues in such devices' firmware are present...

Spiking CA-CFAR Implementation for Radar Target Detection

Radar systems have been used for decades to detect targets on the ground and in the air. The radar signal is transformed into a range-doppler image that distinguishes each detected object by range and velocity for further processing. A target detection algorithm is used to filter noise and clutter. Each target can be in a region with a different...

Exploring Feasibility of FPGAs in Implantable Medical Devices

Implantable Medical Devices (IMDs) are deployed in patients to treat a range of medical conditions. Technological advancements have enabled manufacturers to fit IMDs with specialized hardware that accelerates compute-intensive medical therapies next to a software-run host processor. However, mostly hardware acceleration is found in the form of...

RRAM-based fault-tolerant Binary Neural Networks

Computation-In-Memory (CIM) employing Resistive-RAM<br/>(RRAM)-based crossbar arrays is a promising solution to implement Neural Networks (NNs) on hardware, such that they are efficient with respect to consumption of energy, memory, computational resources, and computation time. In this respect, Binary NNs (BNNs), where the weights obtain single...