SiFive’s Post

Attend this upcoming webinar to dive into NPU architecture, including how it works, its features, advantages, and capabilities in accelerating neural network computations on Intel Core Ultra processors. Machine learning engineer Alessandro Palla will walk you through the practical aspects of deploying performant LLM apps, as well as fast LLM prototyping with the Intel NPU Acceleration Library. Register now: https://intel.ly/3VsuIUq #ArtificialIntelligence #LargeLanguageModel #NeuralProcessingUnit

Here is my latest blog about: Unlock Compute Efficiency with Intel OneAPI - https://lnkd.in/gCbrTqDS Unlocking Compute Efficiency with Intel OneAPI: Empowering Creativity with Skrots Are you ready to take your computing capabilities to the next level? Are you tired of being limited by traditional programming models? Look no further - we have the perfect solution for you! Skrots is here to revolutionize your creative endeavors with Intel OneAPI, unleashing the power of parallel computing like nev... Do Like & Share :)

Looking to speed up #ML development with #SyntheticData? ⚡ Learn from Edge Impulse and NVIDIA Omniverse experts in this webinar on May 14.

Here is my latest blog about: Intel SYCL: Tips for Effective Computational Task Partitioning - https://lnkd.in/geaaHtBn Are you a developer looking for tips on effective computational task partitioning with Intel SYCL? Look no further! We at Skrots have got you covered with expert tips and guidance to help you make the most out of your computational tasks. Intel SYCL is a powerful tool for parallel computing, but effectively partitioning computational tasks can be a challenging task. With the right approach, devel... Do Like & Share :)

🚀 Day 5 of the #100daysRTLChallenge: Today, I dived into the fascinating world of 4-bit multipliers! 🧮 ## What Is a 4-Bit Multiplier? A 4-bit multiplier is a digital circuit that allows you to multiply two binary numbers, each consisting of 4 bits. These multipliers play a crucial role in various electronic and digital systems, where fast and accurate multiplication with 4-bit numbers is essential. 🌟 ### How Does It Work? - The 4-bit multiplier combines an array of AND gates and adders (both half-adders and full adders). - Since it's a combinational circuit, it's often referred to as an array multiplier. - Here's a simplified overview of the process: 1. AND Gates: Generate partial products by multiplying corresponding bits from the two input numbers. 2. Adders: Sum up the partial products to produce the final result. ### Advantages and Limitations: - Advantages: - Simplicity and speed. - Suitable for small bit widths. - Limitations: - Requires more gates as bit width increases. - Not ideal for large-scale multiplication. Remember, 4-bit multipliers are the workhorses behind efficient binary multiplication! 🚀 #100daysRTLChallenge #DigitalDesign #LogicCircuits #vlsi #ALUlogic #DigitalElectronics #verilog #HdlLearning #PublicLearning #EngineeringJourney #ECE

Subject: Design and Evaluation of Computing in Memory Solutions for AI Hardware Accelerators

It's quite a remarkable coincidence today. The NVIDIA CEO remarked about the uselessness of teaching computer science in the future, https://lnkd.in/gUCwQuH7  and then there was the very creative advertisement from Cadbury. The question is, what will the world do with all the Fredos? https://lnkd.in/d5ZxjcTM With close to three decades of exposure to rural sector I was sitting pretty cozy until reading the book "The Coming Wave,".  Your thoughts. 

🙋♀️ Will you be at NVIDIA #gtc24 next week?! Be sure to catch our Chief Technology Officer, Ben Mabey's talk: “How AI Will Decode Biology to Radically Improve Lives,” including 3 main themes as a recipe for success: ➕high-quality talent ➕deep compute resources ➕rich biological data at scale #techbio #biotech #computationalbiology #drugdiscovery #nvidiagtc https://bit.ly/3IyHniv

🤖 TinyGPT-V: Phi-2 BLIP-2 A revolutionary multimodal large language model (MLLM) designed for universal accessibility and efficient use. Unlike its open-source counterparts like LLava and MiniGPT-4, TinyGPT-V offers remarkable performance while demanding only a 24G GPU for training and an 8G GPU or CPU for inference. Built on Phi-2, it combines a powerful language backbone with pre-trained vision modules from BLIP-2 . With 2.8B parameters, TinyGPT-V undergoes a unique quantization process, making it suitable for local deployment and inference on various 8G devices. https://lnkd.in/gS8raUUZ