Spin-Based Neuron Model with Domain Wall Magnets as Synapse

We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design

The impact of OECD Agricultural trade liberalization on poverty in Uganda

The paper examines the projected impacts of agricultural trade liberalisation by OECD countries on poverty in Uganda and compares them to the poverty impacts of all merchandise trade liberalisation. The overall impact of OECD agricultural trade liberalisation on welfare in Uganda from this simulation is positive in contrast to previous research, nevertheless, the poor appear to be made worse off. The liberalisation of all OECD merchandise trade including non-agricultural commodities reduces welfare for all deciles irrespective of household poverty status, residence and region. The results for global partial merchandise trade liberalisation are similar to those for total trade liberalisation with an overall welfare decline of about 0.5 percent. More specifically, even the modest welfare gains for producers from increased prices seem to be offset by welfare losses from increases in consumer goods. Overall, because of the large subsistence agricultural sector, households tend to experience little or no change in total welfare arising from agricultural price changes. Increases in market value of their agricultural based output tend to be offset by changes in the opportunity cost of their subsistence consumption of the bulk of that output.Microsimulation, agricultural trade liberalization, Uganda , poverty

Determinants of poverty vulnerability in Uganda

Ugandan data shows poverty to be entrenched in rural areas and in large households. Households with heads exposed to education, an improved health status, less reliance on agriculture as the most important source of earnings, access to electricity for lighting and, the presence of markets to sell produce in the community experience improved household well-being. The data also confirms two known stylized facts regarding poverty vulnerability. First, households in the Northern region have a higher probability of being poor than those in Central, Eastern, and Western regions. Second, the ‘annual cropping and cattle northern' and ‘annual cropping and cattle Teso' zones are the agro ecological zones that are positively correlated with poverty vulnerability . The fact that residence in rural areas is associated with higher incidence of poverty suggests that promotion of off-farm employment (for example, through rural electrification) would help reduce vulnerability.Poverty vulnerability, logistic regression, Uganda

Boolean and Non-Boolean Computation With Spin Devices

Recently several device and circuit design techniques have been explored for applying nano-magnets and spin torque devices like spin valves and domain wall magnets in computational hardware. However, most of them have been focused on digital logic, and, their benefits over robust and high performance CMOS remains debatable. Ultra-low voltage, current-switching operation of magneto-metallic spin torque devices can potentially be more suitable for non-Boolean computation schemes that can exploit current-mode analog processing. Device circuit co-design for different classes of non-Boolean-architectures using spin-torque based neuron models in spin-CMOS hybrid circuits show that the spin-based non-Boolean designs can achieve 15X-100X lower computation energy for applications like, image-processing, data-conversion, cognitive-computing, pattern matching and programmable-logic, as compared to state of art CMOS designs.Comment: arXiv admin note: substantial text overlap with arXiv:1206.322

Ultra Low Energy Analog Image Processing Using Spin Neurons

In this work we present an ultra low energy, 'on-sensor' image processing architecture, based on cellular array of spin based neurons. The 'neuron' constitutes of a lateral spin valve (LSV) with multiple input magnets, connected to an output magnet, using metal channels. The low resistance, magneto-metallic neurons operate at a small terminal voltage of ~20mV, while performing analog computation upon photo sensor inputs. The static current-flow across the device terminals is limited to small periods, corresponding to magnet switching time, and, is determined by a low duty-cycle system-clock. Thus, the energy-cost of analog-mode processing, inevitable in most image sensing applications, is reduced and made comparable to that of dynamic and leakage power consumption in peripheral CMOS units. Performance of the proposed architecture for some common image sensing and processing applications like, feature extraction, halftone compression and digitization, have been obtained through physics based device simulation framework, coupled with SPICE. Results indicate that the proposed design scheme can achieve more than two orders of magnitude reduction in computation energy, as compared to the state of art CMOS designs, that are based on conventional mixed-signal image acquisition and processing schemes. To the best of authors' knowledge, this is the first work where application of nano magnets (in LSV's) in analog signal processing has been proposed

Preparticipation physical evaluation: An opportunity for Malawian athletes

No Abstract

Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity

Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic spike events. For realizing such updates in neuromorphic hardware, current implementations either require forward and reverse lookup access to the synaptic connectivity table, or rely on memory-intensive architectures such as crossbar arrays. We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation. A simplified implementation in FPGA, using a single timer variable for each neuron, closely approximates exact STDP cumulative weight updates for neuron refractory periods greater than 10 ms, and reduces to exact STDP for refractory periods greater than the STDP time window. Compared to conventional crossbar implementation, the forward table-based implementation leads to substantial memory savings for sparsely connected networks supporting scalable neuromorphic systems with fully reconfigurable synaptic connectivity and plasticity.Comment: Submitted to BioCAS 201