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A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications
Over the last decade, artificial intelligence has found many applications areas in the society.
A 6.3-Nanowatt-per-Channel 96-Channel Neural Spike Processor for a Movement-Intention-Decoding Brain-Computer-Interface Implant
We design the algorithms for those operations to achieve minimal computation complexity while matching or advancing the accuracy of state-of-art Brain-Computer-Interface sorting and movement decoding.
Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device
Always-on artificial intelligent (AI) functions such as keyword spotting (KWS) and visual wake-up tend to dominate total power consumption in ultra-low power devices.
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning
Recent studies on automatic neural architectures search have demonstrated significant performance, competitive to or even better than hand-crafted neural architectures.
KTAN: Knowledge Transfer Adversarial Network
To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained.
Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement
With the same amount of data storage, our model can train a bigger network having more weights, achieving 1% less test error than the conventional binary neural network learning model.
Dynamic Capacity Estimation in Hopfield Networks
Understanding the memory capacity of neural networks remains a challenging problem in implementing artificial intelligence systems.
Recursive Binary Neural Network Learning Model with 2.28b/Weight Storage Requirement
This enables a device with a given storage constraint to train and instantiate a neural network classifier with a larger number of weights on a chip and with a less number of off-chip storage accesses.
Energy-efficient neuromorphic classifiers
We also show that the energy consumption of the IBM chip is typically 2 or more orders of magnitude lower than that of conventional digital machines when implementing classifiers with comparable performance.
