A 0.04 mm (2) Buck-Boost DC-DC Converter for Biomedical Implants Using Adaptive Gain and Discrete Frequency Scaling Control.

This paper presents the design of a reconfigurable buck-boost switched-capacitor DC-DC converter suitable for use in a wide range of biomedical implants. The proposed converter has an extremely small footprint and uses a novel control method that allows coarse and fine control of the output voltage. The converter uses adaptive gain control, discrete frequency scaling and pulse-skipping schemes to regulate the power delivered to a range of output voltages and loads. Adaptive gain control is used to implement variable switching gain ratios from a reconfigurable power stage and thereby make coarse steps in output voltage. A discrete frequency scaling controller makes discrete changes in switching frequency to vary the power delivered to the load and perform fine tuning when the output voltage is within 10% of the target output voltage. The control architecture is predominately digital and it has been implemented as part of a fully-integrated switched-capacitor converter design using a standard bulk CMOS 0.18 µm process. Measured results show that the converter has an output voltage range of 1.0 to 2.2 V, can deliver up to 7.5 mW of load power and efficiency up to 75% using an active area of only 0.04 mm (2), which is significantly smaller than that of other designs. This low-area, low-complexity reconfigurable power converter can support low-power circuits in biomedical implant applications.

Concept Design for a 1-Lead Wearable/Implantable ECG Front-End: Power Management.

Power supply quality and stability are critical for wearable and implantable biomedical applications. For this reason we have designed a reconfigurable switched-capacitor DC-DC converter that, aside from having an extremely small footprint (with an active on-chip area of only 0.04 mm²), uses a novel output voltage control method based upon a combination of adaptive gain and discrete frequency scaling control schemes. This novel DC-DC converter achieves a measured output voltage range of 1.0 to 2.2 V with power delivery up to 7.5 mW with 75% efficiency. In this paper, we present the use of this converter as a power supply for a concept design of a wearable (15 mm × 15 mm) 1-lead ECG front-end sensor device that simultaneously harvests power and communicates with external receivers when exposed to a suitable RF field. Due to voltage range limitations of the fabrication process of the current prototype chip, we focus our analysis solely on the power supply of the ECG front-end whose design is also detailed in this paper. Measurement results show not just that the power supplied is regulated, clean and does not infringe upon the ECG bandwidth, but that there is negligible difference between signals acquired using standard linear power-supplies and when the power is regulated by our power management chip.

Turn Down That Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron.

We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the previously described Synapto-dendritic Kernel Adapting Neuron (SKAN), a hardware efficient neuron model capable of learning spatio-temporal spike patterns. The resulting neuron model is the first to perform synaptic encoding of afferent signal-to-noise ratio in addition to the unsupervised learning of spatio-temporal spike patterns. The neuron model is particularly suitable for implementation in digital neuromorphic hardware as it does not use any complex mathematical operations and uses a novel shift-based normalization approach to achieve synaptic homeostasis. The neuron's noise compensation properties are characterized and tested on random spatio-temporal spike patterns as well as a noise corrupted subset of the zero images of the MNIST handwritten digit dataset. Results show the simultaneously learning common patterns in its input data while dynamically weighing individual afferents based on their signal to noise ratio. Despite its simplicity the interesting behaviors of the neuron model and the resulting computational power may also offer insights into biological systems.

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.