541 research outputs found
Multilayer spintronic neural networks with radio-frequency connections
Spintronic nano-synapses and nano-neurons perform complex cognitive
computations with high accuracy thanks to their rich, reproducible and
controllable magnetization dynamics. These dynamical nanodevices could
transform artificial intelligence hardware, provided that they implement
state-of-the art deep neural networks. However, there is today no scalable way
to connect them in multilayers. Here we show that the flagship nano-components
of spintronics, magnetic tunnel junctions, can be connected into multilayer
neural networks where they implement both synapses and neurons thanks to their
magnetization dynamics, and communicate by processing, transmitting and
receiving radio frequency (RF) signals. We build a hardware spintronic neural
network composed of nine magnetic tunnel junctions connected in two layers, and
show that it natively classifies nonlinearly-separable RF inputs with an
accuracy of 97.7%. Using physical simulations, we demonstrate that a large
network of nanoscale junctions can achieve state-of the-art identification of
drones from their RF transmissions, without digitization, and consuming only a
few milliwatts, which is a gain of more than four orders of magnitude in power
consumption compared to currently used techniques. This study lays the
foundation for deep, dynamical, spintronic neural networks
Energy efficient hybrid computing systems using spin devices
Emerging spin-devices like magnetic tunnel junctions (MTJ\u27s), spin-valves and domain wall magnets (DWM) have opened new avenues for spin-based logic design. This work explored potential computing applications which can exploit such devices for higher energy-efficiency and performance. The proposed applications involve hybrid design schemes, where charge-based devices supplement the spin-devices, to gain large benefits at the system level. As an example, lateral spin valves (LSV) involve switching of nanomagnets using spin-polarized current injection through a metallic channel such as Cu. Such spin-torque based devices possess several interesting properties that can be exploited for ultra-low power computation. Analog characteristic of spin current facilitate non-Boolean computation like majority evaluation that can be used to model a neuron. The magneto-metallic neurons can operate at ultra-low terminal voltage of ∼20mV, thereby resulting in small computation power. Moreover, since nano-magnets inherently act as memory elements, these devices can facilitate integration of logic and memory in interesting ways. The spin based neurons can be integrated with CMOS and other emerging devices leading to different classes of neuromorphic/non-Von-Neumann architectures. The spin-based designs involve `mixed-mode\u27 processing and hence can provide very compact and ultra-low energy solutions for complex computation blocks, both digital as well as analog. Such low-power, hybrid designs can be suitable for various data processing applications like cognitive computing, associative memory, and currentmode on-chip global interconnects. Simulation results for these applications based on device-circuit co-simulation framework predict more than ∼100x improvement in computation energy as compared to state of the art CMOS design, for optimal spin-device parameters
Quantum materials for energy-efficient neuromorphic computing
Neuromorphic computing approaches become increasingly important as we address
future needs for efficiently processing massive amounts of data. The unique
attributes of quantum materials can help address these needs by enabling new
energy-efficient device concepts that implement neuromorphic ideas at the
hardware level. In particular, strong correlations give rise to highly
non-linear responses, such as conductive phase transitions that can be
harnessed for short and long-term plasticity. Similarly, magnetization dynamics
are strongly non-linear and can be utilized for data classification. This paper
discusses select examples of these approaches, and provides a perspective for
the current opportunities and challenges for assembling quantum-material-based
devices for neuromorphic functionalities into larger emergent complex network
systems
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