Anup Dasari’s Post

Rethink how and where AI happens. We're no longer constrained to data centers running complex AI models to arrive at the answers we need at the edge in real time. GPUs are not always the best solution and understanding the full breadth and depth of Intel Core Ultra processors allows you to exploit the value of AI running at the client level. https://lnkd.in/eijsafHJ

This Silicon Semiconductor article dives into Phison's latest addition to the IMAGIN+ Platform: aiDAPTIV+! With over 23 years of experience in NAND controllers, Phison has seamlessly integrated advanced machine learning technology into its controllers and algorithms, improving storage and computing performance. Learn more about its use cases and applications of aiDAPTIV+AOI and the results companies like ADLINK Technology and Giga Computing have shared as early adopters! Read here: https://lnkd.in/gFAnhCpA #ml #techinnovation #computing #storage #ssd #nand #ai

At Phison, our commitment to pioneering innovation and technology is unwavering. This is evident in our substantial investment in R&D initiatives. Our most recent addition to the IMAGIN+ Platform, aiDAPTIV+ AOI, exemplifies this commitment. I invite you to explore its diverse use cases and applications. Find out more here: https://lnkd.in/gzGhVkxH #ML #TechInnovation#Computing #Storage #SSD #NAND #AI "

#Technology #Thread #Semiconductor #Manufacturing #Memory The Semiconductor AI Hybrid Memory: 1/ - There Are At Least Four Ways In Which Memory Organization In A Computer Architecture Helps: -- Data -- Parallelism -- Latency -- Bottleneck ---- 2/ - For AI Applications Running On Modern SoCs: The First Three Are Slowly Turning Into The Fourth One. -- Continuous Compute Demand. Data Movement Is Slow. Increases Processing Costs. -- Parallelism Across Many Many Process Elements. Increasing Chip Complexity. -- Longer And Complex Interconnect Topology. Higher Latency. - Impact: Memory And Compute Bottlenecks. ---- 3/ - To Counter This, The Semiconductor Industry Is Exploring (Not New - Was Proposed A Decade Ago) Hybrid Memory (In-Memory Processing). - Hybrid Memory Takes The Compute Features Of Processing Elements And Embeds It Alongside Memory Cells. - This Provides The SoC With Another Silicon Block That Can Not Only Handle The Data But Also Process It. ---- 4/ - Benefits: -- Increase In Performance -- Saves Energy Via Reduced Data Movement - However, It Also Comes With Some Drawbacks: -- System Software Has To Manage Another Unit For Data Movement Now -- Adds Extra Algorithms To Partition The Data Between The Main Core And The Hybrid Memory Core ---- 5/ - Hybrid Memory Is Still A Work In Progress, With Only Samsung And SK Hynix Showing Promising Results. - But Certainly, The AI Applications Can Benefit From It, As Long As No New Hurdles Are Introduced. - Design, Manufacturing, And Application Costs Also Need To Be Considered. - What Do You Think? ---- #chetanpatil - Chetan Arvind Patil - www.ChetanPatil.in

Solidigm's #AI Field Day presentation highlighted the critical role of large-scale, high-efficiency storage solutions like NAND flash memory in meeting the evolving demands of AI, as covered by tech analyst Ben Young. The company, born from the union of Intel's NAND SSD business and SK hynix, demonstrates expertise in delivering high-density QLC SSDs, such as the D5-P5336 model, which significantly outperforms traditional HDDs in capacity and speed while reducing energy consumption. Solidigm's storage innovations, offering up to 61.44TB per slim drive, not only future-proof AI infrastructure but also promise to drive advancements across various industries by improving training times and real-time decision-making capabilities. #Sponsored #AIFD4

#Technology #Thread #Semiconductor #Manufacturing #SoC The Semiconductor MtM SoC: 1/ - The Semiconductor Industry Has Enjoyed The Success Of Doubling (Every Two Years) The Number Of Transistors In A Silicon Chip. - Which Has Allowed Semiconductor Companies Worldwide To Offer Novel Semiconductor Products And Solutions. It Is Exactly What Moore’s Law Predicted When It Was Proposed Around Four Decades Ago. ---- 2/ - However, As The Semiconductor World Marches Towards 3nm Mass Production (With 2nm Already Showcased By IBM), There Is A Growing Concern About Whether Or Not Moore’s Law Will Keep Pace With The Advancement In The Technology (Mainly Shrinking Transistor Size) And What Are The Alternate Solutions. - The Answer To This Problem Lies In The Different Unique Solutions That The Semiconductor Industry Has Been Working Around In The Last Couple Of Decades. - The Semiconductor Industry Knew There Was Going To Be A Time When Moore’s Law Would Not Be As Applicable As It Is Today, And A Course Correction Would Be Needed. ---- 3/ - This Course Correction Has Led To Numerous Design To Manufacturing Changes That Have Enabled Silicon Chips To Provide More Performance And Better Power Consumption Without Compromising On The Area. - These Solutions Have Been Built On Top Of Different Semiconductor Product Development Processes, Which Have Come Together To Drive Next-Gen Workloads Without Worrying About The Future Implications Of Moore’s Law. ---- 4/ - Some Of The Promising MtM SoC Solutions Are: -- Wafer-Scale Engine SoCs: Turn Full Wafer Into A Chip, Mainly As An Alternate To The Chips That Cannot Cater To AI Workload. -- Chiplets With RISC-V SoCs: RISC-V Chiplets Offer Modularity And Customizability In Computing, Outperforming Existing Silicon Design In Certain Applications By Tailoring Each Chiplet To Specific Workloads. Eliminating The IP And Area Constraints. -- Tensor Processing Units SoCs: TPUs Are Specialized ASICs Designed For Machine Learning, Outperforming GPUs In Neural Network Processing By Integrating All Components On A Single Chip, Offering High Efficiency In Operations Per Watt And Reduced Latency. -- PIM SoCs: PIM Technology Integrated Into SoCs Challenges Traditional Architectures By Embedding Processing In Memory Chips, Reducing Data Transfer Time And Energy, And Potentially Outperforming Existing AI SoCs In Applications Requiring Rapid Parallel Processing Of Large Data Sets. ---- 5/ - What Do You Think About Above MtM SoC As An Alternative To Aggregated SoCs? ---- #chetanpatil - Chetan Arvind Patil - www.ChetanPatil.in

⁉️ARM Chip Maker AI Revenue Poses Questions Despite Their Higher Valuation 🌱In recent times, ARM has undergone a prominent rise in its revenue, from its products and services related to AI. The semiconductor industry is experiencing rapid growth and innovation, driven by advancements in AI and machine learning (ML). Read more here👉https://lnkd.in/gD7aDisJ 💹As AI has become more integrated into customer electronics and industrial tools, demand for software solutions and AL-optimized chips has increased. AEM Holding is a British company in semiconductor and software design. It is widely known for its ARM architecture, which mainly uses microprocessor designs and System-on-chip devices. Arm Jun'ichi Aoyama Saumil Shah Spencer Collins Emma-Jayne Spruce Stacey Spinetti #arm #ai #machinelearning

From commercial to cloud-based acceleration, the latest AI hardware helps designers build bigger, better AI models. Many big tech companies are turning to dedicated AI acceleration to support the current and expected AI loads at both the data center and edge levels. While AI can certainly be deployed with traditional processors, dedicated hardware affords the scalability and performance necessary to develop more advanced AI models.

AI at the Edge may finally get Telcoms compute and storage resources deployed alongside Distributed Units (DUs) and Edge Routers. In the Cloud the "battle for commercial AI" is just beginning. Both between Google DeepMind's Gemini Pro on GCS Vertex and OpenAI's GPT-4 Turbo hosted on MS Azure OpenAI Service; and between 'neutral' LLM players like Oracle Cloud and AWS Bedrock as well as vertical market specialists like Kyndryl and C3.ai. Here's to steadily monetizing AI - especially for Telecoms and Cloud services - in 2024!

In an era where artificial intelligence is transforming industries at an unprecedented pace, the demand for efficient and high-performance hardware has never been greater. Enter VLSI technology, the driving force behind the innovation of AI hardware accelerators. These miniature powerhouses are revolutionizing the way we process data, enabling machines to learn, reason, and make decisions faster than ever before. #artificialintelligence #accelerators #vlsi