Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short and long term memory, non-linear fast response and relatively small footprint. Here we report how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions enable to emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic neuron response in a dense Neural Network (NN). The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks (SNN) to sub-100nm size elements

Translational research for tuberculosis elimination: priorities, challenges, and actions

Christian Lienhardt and colleagues describe the research efforts needed to end the global tuberculosis epidemic by 2035

A TetR-like regulator broadly affects the expressions of diverse genes in Mycobacterium smegmatis

Transcriptional regulation plays a critical role in the life cycle of Mycobacterium smegmatis and its related species, M. tuberculosis, the causative microbe for tuberculosis. However, the key transcriptional factors involved in broad regulation of diverse genes remain to be characterized in mycobacteria. In the present study, a TetR-like family transcriptional factor, Ms6564, was characterized in M. smegmatis as a master regulator. A conserved 19 bp-palindromic motif was identified for Ms6564 binding using DNaseI footprinting and EMSA. A total of 339 potential target genes for Ms6564 were further characterized by searching the M. smegmatis genome based on the sequence motif. Notably, Ms6564 bound with the promoters of 37 cell cycle and DNA damage/repair genes and regulated positively their expressions. The Ms6564-overexpressed recombinant strain yielded 5-fold lower mutation rates and mutation frequencies, whereas deletion of Ms6564 resulted in ∼5-fold higher mutation rates for the mutant strain compared with the wild-type strain. These findings suggested that Ms6564 may function as a global regulator and might be a sensor necessary for activation of DNA damage/repair genes

Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short and long term memory, non-linear fast response and relatively small footprint. Here we report how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions enable to emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic neuron response in a dense Neural Network (NN). The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks (SNN) to sub-100nm size elements

Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses

Extracting information from radio-frequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiple frequencies. Here, we show that magnetic tunnel junctions can process analog RF inputs with multiple frequencies in parallel and perform synaptic operations. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals, using experimental data from magnetic tunnel junctions functioning as both synapses and neurons. We achieve the same accuracy as an equivalent software neural network. These results are a key step for embedded RF artificial intelligence