Sökning: "Neuromorphic Hardware"
Visar resultat 1 - 5 av 8 avhandlingar innehållade orden Neuromorphic Hardware.
1. SiLago: Enabling System Level Automation Methodology to Design Custom High-Performance Computing Platforms : Toward Next Generation Hardware Synthesis Methodologies
Sammanfattning : .... LÄS MER
2. Ag2S-Based Flexible Memristors for Neuromorphic Computing
Sammanfattning : Memristive crossbar arrays hold the great promise for fast and energy efficient neuromorphic computing due to their parallel data storage and processing capabilities. As the key component, memristor should achieve stable resistance switching (RS) characteristics with low energy inputs and be compatible with complementary metal–oxide–semiconductor (CMOS) technology. LÄS MER
3. Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems
Sammanfattning : Mixed-signal neuromorphic processors have brain-like organization and device physics optimized for emulation of spiking neural networks (SNNs), and offer an energy-efficient alternative for implementing artificial intelligence in applications where deep learning based on conventional digital computing is unfeasible or unsustainable. However, efficient use of such hardware requires appropriate configuration of its inhomogeneous, analog neurosynaptic circuits, with methods for sparse, spike-timing-based information encoding and processing. LÄS MER
4. Synchoros VLSI Design Style
Sammanfattning : Computers have become essential to everyday life as much as electricity, communications and transport. That is evident from the amount of electricity we spend to power our computing systems. According to some reports it is estimated to be ≈ 7% of the total consumption worldwide. LÄS MER
5. Using Inhomogeneous Neuronal–Synaptic Dynamics for Spatiotemporal Pattern Recognition in Neuromorphic Processors
Sammanfattning : Mixed-signal neuromorphic processors emulate the electrochemical dynamics of neurons and synapses using conventional analog CMOS-transistor technology and have potential for ultra-low-power machine learning and inference. However, the energy-efficiency of such systems is dependent on sparse, time-based information encoding and processing, and they are, furthermore, subject to imprecision from “device mismatch” in the analog circuitry. LÄS MER