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This paper presents a scalable backpropagation-based algorithm for training deep convolutional networks to run on thermodynamic Ising hardware, achieving 94.9% on CIFAR-10 and 76.0% on CIFAR-100 while analyzing inference cost-accuracy tradeoffs.
Un-0 is an image generator powered by a simulated system of coupled oscillators, achieving FID 6.74 on ImageNet 64×64, matching early conventional methods. It is open-source and aims to demonstrate energy-efficient AI on physical substrates.
A brain-inspired AI architecture promises to deliver faster computing while consuming far less power, potentially advancing energy-efficient AI hardware.
Researchers develop a brain-inspired phototransistor that senses and stores data, potentially reducing AI energy consumption.
Introduces SpikF-GO, a spiking neural network model for multivariate time series forecasting that combines graph-based inter-variable dependency modeling with spike-driven spectral processing, achieving state-of-the-art results among SNN methods with reduced energy consumption.
Otters++ is a novel optical spiking Transformer that leverages time-to-first-spike coding and physical hardware decay to achieve energy-efficient inference, achieving 84.17% on GLUE while maintaining a clear energy advantage over prior spiking Transformer baselines.
This paper presents the first end-to-end RAG pipeline running entirely on a mobile NPU (Qualcomm Hexagon on Snapdragon X Elite), achieving up to 18x faster LLM prefilling and 4x lower energy vs. CPU, with no quality regression.
FuriosaAI's RNGD AI chip is adopted by LG AI Research for their EXAONE platform, offering 2.25x better inference performance and improved energy efficiency, marking a rare major enterprise endorsement of a rival to Nvidia.
Researchers at the University of Rochester developed a solar-thermal desalination method using laser-etched black metal that efficiently produces fresh water without chemical additives and transforms leftover salts into useful materials, avoiding brine waste.
TRINE is a single-bitstream FPGA accelerator and compiler for end-to-end multimodal inference, unifying diverse layers and incorporating runtime-adaptive compute modes, token pruning, and dependency-aware offloading, achieving up to 22.57x latency reduction over an RTX 4090 at 20-21W.
FusionSense introduces a tri-stage near-sensor learning framework for multimodal edge intelligence that jointly reduces compute and communication by using fusion-aware filtering, achieving up to 33× energy savings and significant data-reduction gains on RGB-Depth/LiDAR tasks.
This paper introduces TwELL and Hybrid sparse formats with custom CUDA kernels to efficiently leverage unstructured sparsity in LLMs, achieving over 20% faster training and inference on H100 GPUs while reducing energy and memory usage.
Skymizer announces the HTX301, a PCIe inference card capable of running 700B-parameter LLMs on-premises with high memory and low power consumption.