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Found 4 papers, 1 papers with code
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
Always-Sparse Training by Growing Connections with Guided Stochastic Exploration
Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training.
Low-Power Neuromorphic Hardware for Signal Processing Applications
In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
A fundamental challenge in machine learning today is to build a model that can learn from few examples.
