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Mipsology Zebra on Xilinx FPGA Beats GPUs, ASICs for ML Inference Efficiency - Embedded Computing Design
Mipsology Zebra on Xilinx FPGA Beats GPUs, ASICs for ML Inference Efficiency - Embedded Computing Design

FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks - Xilinx & Numenta
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks - Xilinx & Numenta

11 TOPS photonic convolutional accelerator for optical neural networks | Nature
11 TOPS photonic convolutional accelerator for optical neural networks | Nature

Hailo-8™ AI Processor For Edge Devices | Up to 26 Tops Hardware
Hailo-8™ AI Processor For Edge Devices | Up to 26 Tops Hardware

Transforming Edge AI with Clusters of Neural Processing Units - Embedded Computing Design
Transforming Edge AI with Clusters of Neural Processing Units - Embedded Computing Design

Atomic, Molecular, and Optical Physics | Department of Physics | City University of Hong Kong
Atomic, Molecular, and Optical Physics | Department of Physics | City University of Hong Kong

11 TOPS photonic convolutional accelerator for optical neural networks | Nature
11 TOPS photonic convolutional accelerator for optical neural networks | Nature

11 TOPS photonic convolutional accelerator for optical neural networks | Nature
11 TOPS photonic convolutional accelerator for optical neural networks | Nature

A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm | Research
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm | Research

PowerVR Series3NX Neural Network Accelerator Announced - PC Perspective
PowerVR Series3NX Neural Network Accelerator Announced - PC Perspective

PDF) BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W
PDF) BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W

Not all TOPs are created equal. Deep Learning processor companies often… | by Forrest Iandola | Analytics Vidhya | Medium
Not all TOPs are created equal. Deep Learning processor companies often… | by Forrest Iandola | Analytics Vidhya | Medium

TOPS, Memory, Throughput And Inference Efficiency
TOPS, Memory, Throughput And Inference Efficiency

11 TOPS photonic convolutional accelerator for optical neural networks | Nature
11 TOPS photonic convolutional accelerator for optical neural networks | Nature

11 TOPS photonic convolutional accelerator for optical neural networks | Nature
11 TOPS photonic convolutional accelerator for optical neural networks | Nature

Rockchip RK3399Pro SoC Integrates a 2.4 TOPS Neural Network Processing Unit for Artificial Intelligence Applications - CNX Software
Rockchip RK3399Pro SoC Integrates a 2.4 TOPS Neural Network Processing Unit for Artificial Intelligence Applications - CNX Software

A 617-TOPS/W All-Digital Binary Neural Network Accelerator in 10-nm FinFET CMOS | Semantic Scholar
A 617-TOPS/W All-Digital Binary Neural Network Accelerator in 10-nm FinFET CMOS | Semantic Scholar

As AI chips improve, is TOPS the best way to measure their power? | VentureBeat
As AI chips improve, is TOPS the best way to measure their power? | VentureBeat

Bigger, Faster and Better AI: Synopsys NPUs - SemiWiki
Bigger, Faster and Better AI: Synopsys NPUs - SemiWiki

Measuring NPU Performance - Edge AI and Vision Alliance
Measuring NPU Performance - Edge AI and Vision Alliance

TOPS, Memory, Throughput And Inference Efficiency
TOPS, Memory, Throughput And Inference Efficiency

Imagination Announces First PowerVR Series2NX Neural Network Accelerator Cores: AX2185 and AX2145
Imagination Announces First PowerVR Series2NX Neural Network Accelerator Cores: AX2185 and AX2145

Paper Title (use style: paper title)
Paper Title (use style: paper title)

PDF] A 3.43TOPS/W 48.9pJ/pixel 50.1nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS | Semantic Scholar
PDF] A 3.43TOPS/W 48.9pJ/pixel 50.1nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS | Semantic Scholar