nBMS, a Neuromorphic Battery Management System with a Silicon-Validated Spiking State-of-Charge Core for eVTOL Aircraft

• URL: http://arxiv.org/abs/2609.13506v1
• Date: Fri, 11 Sep 2026 20:12:16 GMT
• Title: nBMS, a Neuromorphic Battery Management System with a Silicon-Validated Spiking State-of-Charge Core for eVTOL Aircraft
• Abstract summary: Event-driven spiking network is designed for per-timestep state-of-charge estimation.<n>System is deployed on a low-cost automotive-qualified Artix-7 field-programmable gate array.<n>Fully annotated post-implementation analysis gives 1.30$$J per inference, an average of 0.65$$W at the 0.5Hz mission cadence.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-of-charge (SoC) estimation for electric vertical take-off and landing (eVTOL) aircraft must run on the vehicle under hard energy and certification budgets, on a duty cycle unlike anything in the automotive literature. In this study an event-driven spiking network, the state-estimation core of the nBMS neuromorphic battery management architecture, is designed for per-timestep SoC estimation and evaluated on a public 22-cell eVTOL dataset with an automotive cross-check. A delta and population encoder, a second-order sigma-delta spiking layer, and a rate-accumulator readout hold 34{,}433 int16 parameters with zero dense multiply-accumulate (MAC) operations. The estimator reaches 2.45\% root-mean-square error (RMSE) against 1.74\% for a tuned long short-term memory (LSTM) baseline, a gap characterized as a temporal-mixing limit in cruise; in exchange it degrades 1.6 times slower than an adaptive leaky integrate-and-fire control under sensor noise and needs roughly 3{,}500 additions per step where the LSTM needs 67{,}700 multiply-accumulates. The full core is deployed on a low-cost automotive-qualified Artix-7 field-programmable gate array at 87\% block-RAM utilization, meets timing at 50~MHz, and reproduces the frozen fixed-point reference bit-exactly over a real 491-step flight segment on silicon. Fully annotated post-implementation analysis gives 1.30~$μ$J per inference, an average of 0.65~$μ$W at the 0.5~Hz mission cadence.

Related papers

• TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer [19.298838240329292]
  TIO-FORMER is a camera-free, optical-flow navigation framework for nano-UAVs.<n>It reduces open-loop position error by 54.4% compared to nano-UAV optical flow.<n>It provides practical anchoring for resource-constrained micro-aerial robots.
  arXiv  Detail & Related papers  (2026-09-15T13:54:06Z)
• SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models [40.64972837908431]
  SoRoMoX implements articulated, Piecewise Constant Strain, and Variable Strain models through a unified, control-ready interface.<n> Sequential CPU rollouts are up to 18.1x faster than state-of-the-art alternatives, while GPU-parallel rollouts increase throughput by up to 234.6x.
  arXiv  Detail & Related papers  (2026-08-06T23:31:47Z)
• Cutting AI Datacenter Energy with Reinforcement Learning: Measured Power Control of LLM Training from One GPU to the Fleet [0.0]
  We instrument GRPO training with half-second power telemetry at 7B, 14B, and 72B scales on one to four A100s (380,000+ samples)<n>We train a PPO meta-controller that adapts the workload's own generation parameters to measured power.<n>We quantify the economic and carbon consequences and specify a low-cost operator pilot.
  arXiv  Detail & Related papers  (2026-07-27T20:51:03Z)
• Multi-Domain Physics-Based MDO of Multirotor UAVs: A Deterministic Framework for Discrete COTS Sizing [1.6114012813668932]
  This paper presents AeroEval, a physics-based, Multidisciplinary Design Optimization (MDO) engine.<n>Engine maps continuous sizing optima to physically purchasable, commercial off-the-shelf (COTS) components.<n>Battery mass is predicted within 7.9% on single-pack platforms.
  arXiv  Detail & Related papers  (2026-07-24T03:52:36Z)
• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment [0.0]
  On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.<n>We present a benchmark comparing traditional ML methods against lightweight transformer architectures for binary fault detection.<n>Our results reveal that on well-separated sensor data, lightweight transformers match traditional ML at 87.8% F1 but at 100x the model size and 9000x the latency.
  arXiv  Detail & Related papers  (2026-06-23T05:54:33Z)
• E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference [0.0]
  E-ReCON is a 16 Kb digital compute-in-memory (DCIM) macro based on a compact 3T1R ReRAM bitcell for edge-AI inference.<n>A novel interleaved 10T/28T adder tree is introduced, reducing transistor count and power consumption by 37% and 28%, respectively.<n>The proposed macro achieves a minimum latency of 0.48 ns, throughput of 2.31-3.1 TOPS, and energy efficiency of up to 419 TOPS/W.
  arXiv  Detail & Related papers  (2026-05-20T05:18:27Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration [0.12314765641075437]
  EULER-ADAS is a SIMD-enabled logarithmic bounded-Posit neural compute engine for energyefficient and reliability-aware ADAS acceleration.<n>The proposed datapath combines bounded-regime Posit representation, stageadaptive logarithmic mantissa multiplication with bit truncation, and a SIMD-shared quire accumulation path.<n> FPGA implementation shows that the proposed configurations reduce LUT count by up to 41.4%, delay by up to 76.1%, and power by up to 71.9% relative to exact Posit neural compute engines.
  arXiv  Detail & Related papers  (2026-05-07T19:20:10Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots [36.62645789321095]
  This paper develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots.<n>An indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads.<n>The robot successfully manages dynamic payloads with 73% of its mass on rough terrains.
  arXiv  Detail & Related papers  (2026-03-09T18:43:31Z)
• Benchmarking IoT Time-Series AD with Event-Level Augmentations [34.864214444544565]
  We introduce an evaluation protocol with unified event-level augmentations that simulate real-world issues.<n>We evaluate 14 representative models on five public anomaly datasets.
  arXiv  Detail & Related papers  (2026-02-17T09:45:44Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.