11 TOPS photonic convolutional accelerator for optical neural networks | Nature
PowerVR Series3NX Neural Network Accelerator Announced - PC Perspective
As AI chips improve, is TOPS the best way to measure their power? | VentureBeat
TOPS, Memory, Throughput And Inference Efficiency
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks - Xilinx & Numenta
Transforming Edge AI with Clusters of Neural Processing Units - Embedded Computing Design
Synopsys' ARC Embedded Vision Processors Delivers Industry-Leading 35 TOPS Performance for AI | Maker Pro
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
A 617-TOPS/W All-Digital Binary Neural Network Accelerator in 10-nm FinFET CMOS | Semantic Scholar
Mipsology Zebra on Xilinx FPGA Beats GPUs, ASICs for ML Inference Efficiency - Embedded Computing Design
Looking Beyond TOPS/W: How To Really Compare NPU Performance
Electronics | Free Full-Text | Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey
A 161.6 TOPS/W Mixed-mode Computing-in-Memory Processor for Energy-Efficient Mixed-Precision Deep Neural Networks (유회준교수 연구실) - KAIST 전기 및 전자공학부
Imagination Announces First PowerVR Series2NX Neural Network Accelerator Cores: AX2185 and AX2145
As AI chips improve, is TOPS the best way to measure their power? | VentureBeat
Measuring NPU Performance - Edge AI and Vision Alliance
Micro-combs enable 11 TOPS photonic convolutional neural networ...
![VLSI 2018] A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural- Network Processor with Cell Current Controlled Writing and Flexible Network Architecture](https://t1.daumcdn.net/cfile/tistory/993DBA3E5BAF92CE38 "VLSI 2018] A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural- Network Processor with Cell Current Controlled Writing and Flexible Network Architecture")
VLSI 2018] A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural- Network Processor with Cell Current Controlled Writing and Flexible Network Architecture
EdgeCortix Announces Sakura AI Co-Processor Delivering Industry Leading Low-Latency and Energy-Efficiency | EdgeCortix
TOPS, Memory, Throughput And Inference Efficiency
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm | Research
TOPS: The truth behind a deep learning lie - EDN Asia
Hailo-8™ AI Processor For Edge Devices | Up to 26 Tops Hardware
![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](https://d3i71xaburhd42.cloudfront.net/a2e283532b71e9b6af7addb3b3f4f4a1af6e0fb4/2-Figure1-1.png "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
PDF) BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W
Rockchip RK3399Pro SoC Integrates a 2.4 TOPS Neural Network Processing Unit for Artificial Intelligence Applications - CNX Software
