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Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing
Our evaluation shows QHD capability for real-time learning, providing 34. 6 times speedup and significantly better quality of learning than DQN.
DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression
Data reduction in storage systems is becoming increasingly important as an effective solution to minimize the management cost of a data center.
Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework
Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning.
SHEARer: Highly-Efficient Hyperdimensional Computing by Software-Hardware Enabled Multifold Approximation
In this paper, we propose SHEARer, an algorithm-hardware co-optimization to improve the performance and energy consumption of HD computing.
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework
To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i. e., multiplication, addition, activation functions, and pooling.
