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Despite the advantages of GPUs, the unique demands of neural networks—especially with the advent of more complex models—suggest that advancements propposing even more specialized hardware might be beneficial. Graph Computing: Neural networks can be represented as graphs, with neurons as nodes and synapses as edges. Graph computing hardware directly processes these graphs, optimizing the pathways and flows of data. This direct approach can reduce the redundancy in traditional computing architectures, where data needs to be reshaped to fit the rigid, linear processing pipelines. Neuromorphic Computing: Inspired by the structure and function of the human brain, neuromorphic computing architectures mimic the brain’s neural architectures, potentially leading to more efficient processing for AI applications. Optical Computing: Leveraging the speed of light, optical computing can perform calculations at speeds far exceeding those of electronic hardware, with potentially huge benefits for tasks requiring massive parallel processing, like neural networks. These emerging fields promise to change the fundamental ways hardware handles operations, aligning more closely with the operational paradigms of neural networks. This alignment could lead to significant leaps in performance and efficiency, enabling more complex and capable AI systems. What do you think the future holds for hardware development in AI? #NeuralNetworks #HardwareInnovation #DeepLearning

Here are 5 reasons why #NPUs are a big deal: 🧠 Specialization: NPUs are built just for #AI, making them super efficient for tasks like handling neural networks. They're way faster and use less power than regular #CPUs and #GPUs. 💨 Efficiency: These chips are not just powerful; they're also energy-efficient, which is great for battery life on devices. 🪴 Scalability: As AI gets more complex, NPUs are designed to keep up, helping power more advanced applications without a sweat. 🙌🏻 Impact: Companies like Cephable are using NPUs to make tech more #accessible, improving how apps respond to voice and facial commands. 📈 Metrics: New metrics like TOPS (Tera Operations Per Second) and TPJ (Tokens per Joule) are helping us understand just how effective NPUs are. Ben Bajarin and Max Weinbach from Creative Strategies, Inc. - Consumer Tech Research can teach you a lot more than I can. Check out their report! https://lnkd.in/gxFYhMXD Qualcomm, #snapdragon, #edgeAI,

Dutch chip startup Innatera has showcased its neuromorphic microcontroller for edge AI sensor applications based on the #RISCV #openISA. The Spiking Neural Processor T1 is designed for low power edge AI sensor applications running both neuromorphic and traditional deep neural network AI models. Innatera claims the chip can deliver energy savings of up to 500x with 100x shorter latency across a range of applications compared to a traditional CPU, DSP, or conventional AI accelerator. The Spiking Neural Processor T1 combines analog-mixed signal neuromorphic computing with a RISC-V processor core and support for accelerating traditional convolutional neural network (#CNN) AI models. Other neuromorphic chips have been developed by Intellisense, SynSense, and Renesas, but software support and the availability of models has been a significant issue. To address this, Innatera has developed a software development kit (#SDK) for the Spiking Neural Processor called Talamo. Talamo integrates with the #PyTorch machine learning framework, providing a streamlined and intuitive platform for model development and training. The development process also uses the robust visualization and measurement capabilities of #Tensorboard. It has a growing library of models with a range of pre-configured models, as well as its comprehensive environment for application development and deployment. “#Neuromorphic computing is here, and will redefine intelligence at the sensor-edge. We’re excited to unveil the Spiking Neural Processor and announce the availability of the T1 to customers for pre-production trials,” said Sumeet Kumar, CEO at Innatera. #AI #Sensor #AIaccelerator #edgeai

Neuromorphic computing, a form of artificial intelligence technology, mimics the structure and function of the human brain. This innovative approach shows great promise in revolutionizing the capabilities of AI systems. Discover more at:

AI Research Chronicle Blog Series Post 19: Neuromorphic Computing - Revolutionizing AI with Brain-Inspired Technology Consider a computing paradigm that emulates the neural networks of the human brain. It offers massively parallel processing, adaptive learning, and exceptional energy efficiency. This is the potential of neuromorphic computing, an innovative approach set to revolutionize artificial intelligence and various industries. In this article, we will explore the fascinating world of neuromorphic computing, focusing on its key features such as event-driven computation and low power consumption. We will also discuss the latest advancements in neuromorphic chips, such as Intel Corporation Loihi and IBM TrueNorth, which demonstrate the immense potential of this brain-inspired technology. Neuromorphic computing is poised to unlock new boundaries in edge computing, robotics, healthcare, finance, and beyond. This emerging field enables more intelligent, adaptable, and efficient computing solutions, driving breakthroughs in AI applications across various domains. Join me in exploring the exciting world of neuromorphic computing and its implications for the future of AI. Read the full article here:  https://lnkd.in/eWFwZpPq Don't forget to bookmark my blog for the latest insights on cutting-edge AI research: www.ajithp.com 🌐📖 #NeuromorphicComputing #BrainInspiredTechnology #FutureOfAI #EdgeComputing #ResponsibleAI

Just a moment...: A highly advanced Artificial Intelligence (AI) processor has been developed, utilizing photonic neurons and operating with light rather than electrical signals. It is currently one of the fastest AI processors worldwide. - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

Proud to be featured in the Neuromorphic Computing, Memory and Sensing 2024 report by Yole Group 👉 https://lnkd.in/di5Jvasq 🚩 NimbleAI contributes to meeting the “Neuromorphic 3D sensing” milestone listed in the Yole report by enabling light-field event-driven vision, building on commercial Dynamic Vision Sensors (DVS) from PROPHESEE & Sony. 🔮 Raytrix GmbH & CSIC & imec & IKERLAN have already assembled the world’s first light-field DVS using a commercial Prophesee-Sony IMX636 sensor and a custom Raytrix microlens array. Ultra energy-efficient algorithms for processing neuromorphic light-fields are being designed and validated using real-world ADAS datasets from AVL. These algorithms will next be implemented in silicon in time to meet the “Neuromorphic 3D sensing” milestone by 2026, as foreseen in the Yole report. The expectation is to achieve near-VGA resolution and tens of mm depth accuracy for a dozen metres operational range with sub-ms latency and tens of mW energy consumption. 📈 To help make light-field DVS (and standard DVS) mainstream, IKERLAN is developing BEGI: a hardware component that bridges the neuromorphic and non-neuromorphic worlds. BEGI extracts meaningful information from DVS inputs and composes data structures compatible with AI models optimized for industry-standard processors and AI accelerators. 👁️ The minimalist NimbleAI vision solution is completed with a foveated DVS from CSIC and attentional spiking neural networks from The University of Manchester, which jointly enhance the information efficiency of the data structures composed by BEGI. 💎 RISC-V based downstream computing engines offer customization through Menta SAS eFPGA IP, and efficient near-memory processing by CEA-List, both controlled by a RISC-V CPU from Codasip. This computing setup allows efficient processing of the data structures provided by BEGI and is supported by TinyML frameworks. As an early adopter of this technology, Viewpointsystem is designing tiny neural networks for eye-tracking 👀 that fit in few hundred kB. 💪🏻 To enable more powerful vision solutions using large AI models, Politecnico di Milano & Snap Inc. are developing the virtual neural network (VNN) concept for event-driven AI processors. VNNs harness high-density nonvolatile memory to dynamically swap neural network parts onto AI processors while keeping maximum dataflow performance, virtually extending the processing resources implemented in silicon. NimbleAI will endow next-gen 💻📱👓 🤖 🔭🚗🚊✈️🚀🛸 with biology-like vision! Reach out to discuss how to meet your challenging computer vision and 3D perception requirements with NimbleAI tech. #NeuromorphicAI #edgeAI #eventbasedvision #TinyML #ComputerVision #NearMemoryProcessing #Neuromorphic3Dsensing #LightFieldDVS #VirtualNeuralNetworks

Our CEO Patrick Bowen is interviewed on SemiWiki.com. What problem is Neurophos solving? What will make customers switch from using a GPU, to using our technology? How can you engage with Neurophos today?  What new features & technology are we working on? All this and more, on SemiWiki. https://lnkd.in/dDF59ZxJ #AI #metamaterials #siliconphotonics #ceoinsights

The rise of purpose-built databases over the last decade, such as graph databases or time series databases, is a success story we rarely talk about. We now see a similar diversification trend in compute, with accelerated processors for machine learning - or even specifically for machine learning inference. But these days people are pushing it further and leave the paradigm of integrated circuits (e.g. using optical neural networks) or even classical computing altogether (e.g. quantum processing units). I'm looking forward to a world where the old CPU becomes the orchestrator between numerous different purpose-built processing units in large workloads. Here is a recent proposal for new optical neural networks (ONN) (link in comments). The authors claim that it fulfills many of the criteria we'd want from a new mainstream purpose-built processing unit: 1) Very high compute density: 25 TerraOP/ (s * mm2) compared to ~ 0.1 TerraOP/ (s*mm2) 2) Full-system energy efficiency: ~1 fJ/OP compared to ~1pJ/OP for GPUs 3) Inline non-linearity: Neural networks need nonlinearities to represent nonlinear functions. But this is hard to do with optical elements because phtotons don't interact. 4) Scalable through existing mature wafer-scale fabrication processes and photonic integration 5) Freely scale to run models with up to 10s of billions of neurons I'm wondering if/when I will train my first neural network on an optical neural network. #machinelearning #gpu #cpu #ai