MP-IB uses an 8x information asymmetry via FP16 trait heads and INT4 state heads to disentangle speaker identity from agitation in voice biomarkers, outperforming larger models on edge devices with low latency and suppressed identity leakage.
CMSIS-NN: Efficient neural network kernels for ARM Cortex-M CPUs
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
A fine-grained split inference system enables CNN models infeasible on single MCUs to run across networked devices by partitioning at sub-layer granularity, reducing per-device peak RAM while keeping practical latency.
EdgeSpike delivers 91.4% mean accuracy on five sensing tasks with 31x lower energy on neuromorphic hardware and 6.3x longer battery life in a seven-month field deployment compared to conventional CNNs.
A co-design framework using approximate matrix decomposition and genetic algorithms delivers 33% average latency reduction in TinyML CNN FPGA accelerators with 1.3% average accuracy loss versus standard systolic arrays.
A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
citing papers explorer
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Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection
MP-IB uses an 8x information asymmetry via FP16 trait heads and INT4 state heads to disentangle speaker identity from agitation in voice biomarkers, outperforming larger models on edge devices with low latency and suppressed identity leakage.
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Split CNN Inference on Networked Microcontrollers
A fine-grained split inference system enables CNN models infeasible on single MCUs to run across networked devices by partitioning at sub-layer granularity, reducing per-device peak RAM while keeping practical latency.
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EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures
EdgeSpike delivers 91.4% mean accuracy on five sensing tasks with 31x lower energy on neuromorphic hardware and 6.3x longer battery life in a seven-month field deployment compared to conventional CNNs.
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Co-Design of CNN Accelerators for TinyML using Approximate Matrix Decomposition
A co-design framework using approximate matrix decomposition and genetic algorithms delivers 33% average latency reduction in TinyML CNN FPGA accelerators with 1.3% average accuracy loss versus standard systolic arrays.
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Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.