An On-chip Trainable and Clock-less Spiking Neural Network with 1R Memristive Synapses

Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable Resistive RAM or RRAM array serving as synaptic weights and neuronal drivers in the periphery is an attractive option for SNN. Recognition (akin to reading the synaptic weight) requires small amplitude bias applied across the RRAM to minimize conductance change. Learning (akin to writing or updating the synaptic weight) requires large amplitude bias pulses to produce a conductance change. The contradictory bias amplitude requirement to perform reading and writing simultaneously and asynchronously, akin to biology, is a major challenge. Solutions suggested in the literature rely on time-division-multiplexing of read and write operations based on clocks, or approximations ignoring the reading when coincidental with writing. In this work, we overcome this challenge and present a clock-less approach wherein reading and writing are performed in different frequency domains. This enables learning and recognition simultaneously on an SNN. We validate our scheme in SPICE circuit simulator by translating a two-layered feed-forward Iris classifying SNN to demonstrate software-equivalent performance. The system performance is not adversely affected by a voltage dependence of conductance in realistic RRAMs, despite departing from linearity. Overall, our approach enables direct implementation of biological SNN algorithms in hardware

Quantum walk on a line for a trapped ion

We show that a multi-step quantum walk can be realized for a single trapped ion with interpolation between quantum and random walk achieved by randomizing the generalized Hadamard coin flip phase. The signature of the quantum walk is manifested not only in the ion's position but also its phonon number, which makes an ion trap implementation of the quantum walk feasible.Comment: 5 pages, 3 figure

CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

Adaptive feedback analysis and control of programmable stimuli for assessment of cerebrovascular function

The assessment of cerebrovascular regulatory mechanisms often requires flexibly controlled and precisely timed changes in arterial blood pressure (ABP) and/or inspired CO2. In this study, a new system for inducing variations in mean ABP was designed, implemented and tested using programmable sequences and programmable controls to induce pressure changes through bilateral thigh cuffs. The system is also integrated with a computer-controlled switch to select air or a CO2/air mixture to be provided via a face mask. Adaptive feedback control of a pressure generator was required to meet stringent specifications for fast changes, and accuracy in timing and pressure levels applied by the thigh cuffs. The implemented system consists of a PC-based signal analysis/control unit, a pressure control unit and a CO2/air control unit. Initial evaluations were carried out to compare the cuff pressure control performances between adaptive and non-adaptive control configurations. Results show that the adaptive control method can reduce the mean error in sustaining target pressure by 99.57 % and reduce the transient time in pressure increases by 45.21 %. The system has proven a highly effective tool in ongoing research on brain blood flow control

Efficiency analysis methodology of FPGAs based on lost frequencies, area and cycles

We propose a methodology to study and to quantify efficiency and the impact of overheads on runtime performance. Most work on High-Performance Computing (HPC) for FPGAs only studies runtime performance or cost, while we are interested in how far we are from peak performance and, more importantly, why. The efficiency of runtime performance is defined with respect to the ideal computational runtime in absence of inefficiencies. The analysis of the difference between actual and ideal runtime reveals the overheads and bottlenecks. A formal approach is proposed to decompose the efficiency into three components: frequency, area and cycles. After quantification of the efficiencies, a detailed analysis has to reveal the reasons for the lost frequencies, lost area and lost cycles. We propose a taxonomy of possible causes and practical methods to identify and quantify the overheads. The proposed methodology is applied on a number of use cases to illustrate the methodology. We show the interaction between the three components of efficiency and show how bottlenecks are revealed

Hardware for digitally controlled scanned probe microscopes

The design and implementation of a flexible and modular digital control and data acquisition system for scanned probe microscopes (SPMs) is presented. The measured performance of the system shows it to be capable of 14-bit data acquisition at a 100-kHz rate and a full 18-bit output resolution resulting in less than 0.02-Å rms position noise while maintaining a scan range in excess of 1 µm in both the X and Y dimensions. This level of performance achieves the goal of making the noise of the microscope control system an insignificant factor for most experiments. The adaptation of the system to various types of SPM experiments is discussed. Advances in audio electronics and digital signal processors have made the construction of such high performance systems possible at low cost