Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1705.06942

Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning

Akhilesh Jaiswal·Amogh Agrawal·Priyadarshini Panda·Kaushik Roy
View original source ↗Read source document ↗Export metadata ↓

Abstract

Conventional von-Neumann computing models have achieved remarkable feats for the past few decades. However, they fail to deliver the required efficiency for certain basic tasks like image and speech recognition when compared to biological systems. As such, taking cues from biological systems, novel computing paradigms are being explored for efficient hardware implementations of recognition/classification tasks. The basic building blocks of such neuromorphic systems are neurons and synapses. Towards that end, we propose a leaky-integrate-fire (LIF) neuron and a programmable non-volatile synapse using domain wall motion induced by magneto-electric effect. Due to a strong elastic pinning between the ferro-magnetic domain wall (FM-DW) and the underlying ferro-electric domain wall (FE-DW), the FM-DW gets dragged by the FE-DW on application of a voltage pulse. The fact that FE materials are insulators allows for pure voltage-driven FM-DW motion, which in turn can be used to mimic the behaviors of biological spiking neurons and synapses. The voltage driven nature of the proposed devices allows energy-efficient operation. A detailed device to system level simulation framework based on micromagnetic simulations has been developed to analyze the feasibility of the proposed neuro-synaptic devices. We also demonstrate that the energy-efficient voltage-controlled behavior of the proposed devices make them suitable for dynamic on-line and lifelong learning in spiking neural networks (SNNs).

Explore related subjects

cs.ETcs.NE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Akhilesh Jaiswal, Amogh Agrawal, Priyadarshini Panda, Kaushik Roy. 2017-11-23. Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning. https://arxiv.org/abs/1705.06942

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ASTRA: Toward Agentic AI for Intelligent Device-Network-Cloud Synergy in Next-Generation Mobile Communication

The evolution toward next-generation mobile communication systems demands intelligence-native networks capable of autonomously adapting to user intent, yet the prevailing 3GPP protocol-driven device-network-cloud (DNC) architecture imposes three structural bottlenecks: protocol-constrained decision spaces confining optimization to predefined parameter subsets, cascaded information asymmetry from lossy interface compression that strips semantic context and causes intent miscalibration, and inherently reactive coordination mechanisms that trigger actions only after performance degradation. This paper proposes an autonomous agentic AI paradigm named Agentic Synergy for Telecommunication Resource Autonomy (ASTRA), which introduces a three-tier agent layer, including device agent, network agent, and cloud agent, decoupling network intelligence from the underlying hardware infrastructure. These agents collaborate through bidirectional semantic channels, including semantic intent messages, capability abstraction messages, global directives, and peer coordination, executing a six-phase cycle of perceive, reason and predict, communicate, decide, act, and learn that transforms reactive protocol-driven operations into proactive, intent-calibrated optimization over the full decision space. Validated through system-level simulations in two representative scenarios, ASTRA achieves a 13.1\% average throughput gain in dense-crowd cell selection by redistributing UEs from congested cells via semantic load exchange, and an 18.2\% passive handover reduction in high-speed mobility through predictive trajectory-aware coordination, providing initial evidence that the proposed agentic framework accesses solution regions structurally inaccessible under protocol-constrained architectures.

cs.ET↗
arXiv2026

A Closed-Form Molecule-Release Rule for Diffusion-Based Molecular Communications with Ligand Receptors

The number of molecules released per bit is a fundamental design variable of diffusion-based molecular communication (MC), and ligand-receptor reception breaks the more-is-better intuition. Too few molecules leave the bound-receptor observations buried in binding noise, while too many amplify the accumulated intersymbol interference and saturate the finite receptor population, again making the observations indistinguishable. Reliability therefore peaks in an interior operating region whose location seems to require an exhaustive search over the channel dynamics. In this paper, we show that this search can be obviated for a biologically plausible receiver that compares consecutive bound-receptor counts without channel state information or a decision threshold. We derive a closed-form transmission rule, which sets the number of molecules released per bit such that the receptor dissociation constant equals the geometric mean of the two bit-conditioned received concentration levels, prove that it exactly minimizes the bit error probability of a memoryless binomial receptor model, and express it in the physical channel parameters through an Euler--Maclaurin evaluation of the interference. Time-domain Monte Carlo sweeps of the channel and receptor parameters, corroborated by particle-based simulations, show that the empirically optimal release count coincides with the prediction or lies above it by a small factor.

cs.ET↗
arXiv2026

Composability rather than computation sets the cost of an analog EML hardware fabric

The operator eml(x, y) = exp(x) - ln(y) with the constant 1 generates the elementary functions, a continuous counterpart to NAND. Whether it yields a useful fabric had not been asked of hardware. We ask in network models, circuit simulation and SkyWater 130 nm layout. Four bipolar junctions evaluate the operator for 13 fJ, beating a width-matched digital datapath by 4-134x. The fabric assembled from them is not cheap: it loses to resource-matched baselines, and over the reals its grammar excludes trigonometry. Amplifiers holding those junctions' operating points take 74.5% of a cell's current, so a cell costs 3000 times what they spend. Extracted non-idealities cost 2.6x when a cell must hold a value and nothing when it need only be repeatable. Sharing them across cells recovers two of the three orders. The premise was that a universal primitive licenses a uniform machine. It survives in the primitive and fails in the machine.

cs.ET↗