TOPS, Memory, Throughput And Inference Efficiency
TOPS, Memory, Throughput And Inference Efficiency
Renesas AI accelerator operates at 8.8TOPS/W
Essential AI Terms: Tips for Keeping Up with Industrial DX | CONTEC
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm | Research
MVM for neural network accelerators. (a) Sketch of a fully connected... | Download Scientific Diagram
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
Looking Beyond TOPS/W: How To Really Compare NPU Performance
Rockchip RK3399Pro SoC Integrates a 2.4 TOPS Neural Network Processing Unit for Artificial Intelligence Applications - CNX Software
Imagination Announces First PowerVR Series2NX Neural Network Accelerator Cores: AX2185 and AX2145
TOPS: The truth behind a deep learning lie - EDN Asia
VeriSilicon Launches VIP9000, New Generation of Neural Processor Unit IP | Markets Insider
![PDF] A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/f2dd73ae127c5ee3713a92e1057eddea92fbf207/2-Figure1-1.png "PDF] A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets | Semantic Scholar")
PDF] A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets | Semantic Scholar
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks - Xilinx & Numenta
![PDF] A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/99d272ce0028fc67d6b6165633b5fb767f003a3d/1-Figure1-1.png "PDF] A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm | Semantic Scholar")
PDF] A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm | Semantic Scholar
Synopsys ARC NPX6 NPU Family for AI / Neural Processing
Atomic, Molecular, and Optical Physics | Department of Physics | City University of Hong Kong
Measuring NPU Performance - Edge AI and Vision Alliance
Electronics | Free Full-Text | Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey
![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
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
As AI chips improve, is TOPS the best way to measure their power? | VentureBeat
![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
Mipsology Zebra on Xilinx FPGA Beats GPUs, ASICs for ML Inference Efficiency - Embedded Computing Design
EdgeCortix Announces Sakura AI Co-Processor Delivering Industry Leading Low-Latency and Energy-Efficiency | EdgeCortix
Not all TOPs are created equal. Deep Learning processor companies often… | by Forrest Iandola | Analytics Vidhya | Medium
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm | Research
