{"id":1823,"date":"2024-09-13T06:00:11","date_gmt":"2024-09-13T06:00:11","guid":{"rendered":"http:\/\/localhost:8080\/sk-hynix-presents-upgraded-aimx-solution-at-ai-hw-edge-ai-summit-2024\/"},"modified":"2026-06-11T17:10:12","modified_gmt":"2026-06-11T17:10:12","slug":"sk-hynix-presents-upgraded-aimx-solution-at-ai-hw-edge-ai-summit-2024","status":"publish","type":"post","link":"https:\/\/news.skhynix.com\/en\/sk-hynix-presents-upgraded-aimx-solution-at-ai-hw-edge-ai-summit-2024\/","title":{"rendered":"SK hynix Presents Upgraded AiMX Solution at AI Hardware & Edge AI Summit 2024"},"content":{"rendered":"

\"A<\/p>\n

\u25b2 A glimpse of SK hynix\u2019s booth at the AI Hardware & Edge AI Summit 2024<\/p>\n

SK hynix unveiled an enhanced Accelerator-in-Memory based Accelerator (AiMX) card at the AI Hardware & Edge AI Summit 2024 held September 9\u201312 in San Jose, California. Organized annually by Kisaco Research, the summit brings together representatives from the AI and machine learning ecosystem to share industry breakthroughs and developments. This year\u2019s event focused on exploring cost and energy efficiency across the entire technology stack.<\/p>\n

Marking its fourth appearance at the summit, SK hynix highlighted how its AiM* <\/sup> products can boost AI performance across data centers and edge devices* <\/sup>.<\/p>\n

* <\/sup>Accelerator in Memory (AiM): SK hynix\u2019s PIM semiconductor product name, which includes GDDR6-AiM.
\n* <\/sup>Edge device: Hardware that controls the flow of data at the boundary between two networks. While they fulfill numerous roles, edge devices essentially serve as the entry or exit point to a network.<\/p>\n

\"Attendees<\/p>\n

\u25b2 Attendees gather to learn more about the upgraded AimX card<\/p>\n

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Booth Highlights: Meet the Upgraded AiMX<\/h3>\n
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In the AI era, high-performance memory products are vital for the smooth operation of LLMs* <\/sup>. However, as these LLMs are trained on increasingly larger datasets and continue to expand, there is a growing need for more efficient solutions. SK hynix addresses this demand with its PIM* <\/sup> product AiMX, an AI accelerator card that combines multiple GDDR6-AiMs\u00a0to provide high bandwidth and outstanding energy efficiency.<\/p>\n

* <\/sup>Large language model (LLM): Advanced AI systems that require extensive datasets to train models to understand and generate human-like language. It enables applications like natural language processing and translation.
\n* <\/sup>Processing-In-Memory (PIM): A next-generation technology that embeds processing capabilities within memory, minimizing data transfer between the processor and memory. This boosts efficiency and speed, especially for data-intensive tasks like LLMs, where quick data access and processing are essential.<\/p>\n

\"The<\/p>\n

\u25b2 The 32 GB AiMX prototype card was shown publicly for the first time at the event<\/p>\n

At the AI Hardware & Edge AI Summit 2024, SK hynix presented its updated 32 GB AiMX prototype which offers double the capacity of the original card featured at last year\u2019s event. To highlight the new AiMX\u2019s advanced processing capabilities in a multi-batch* <\/sup> environment, SK hynix held a demonstration of the prototype card with the Llama 3* <\/sup> 70B model, an open source LLM. In particular, the demonstration underlined AiMX\u2019s ability to serve as a highly effective attention* <\/sup> accelerator in data centers.<\/p>\n

* <\/sup>Multi-batch: A computer processing method in which the system groups together multiple tasks (batches) and processes them at once.
\n* <\/sup>Llama 3: An open source LLM developed by Meta, featuring pretrained and instruction-fine-tuned language models.
\n* <\/sup>Attention: Mechanisms which give LLMs context about text, lessening the model\u2019s chance of misunderstandings and allowing it to generate more accurate and contextually relevant outputs.<\/p>\n

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\u25b2 The upgraded AiMX was demonstrated with the Llama 3 70B model LLM to highlight its processing capabilities<\/p>\n

AiMX addresses the cost, performance, and power consumption challenges associated with LLMs in not only data centers, but also in edge devices and on-device AI applications. For example, when applied to mobile on-device AI applications, AiMX improves LLM speed three-fold compared to mobile DRAM while maintaining the same power consumption.<\/p>\n

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Featured Presentation: Accelerating LLM Services from Data Centers to Edge Devices\u200b<\/h3>\n
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\u25b2 Euicheol Lim presenting on how the AiMX system accelerates LLM services<\/p>\n

On the final day of the summit, SK hynix gave a presentation detailing how AiMX is an optimal solution for accelerating LLM services in data centers and edge devices. Euicheol Lim, research fellow and head of the Solution Advanced Technology team, shared the company\u2019s plans to develop AiM products for on-device AI based on mobile DRAM and revealed the future vision for AiM. In closing, Lim emphasized the importance of close collaboration with companies involved in developing and managing data centers and edge systems to further advance AiMX products.<\/p>\n

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Looking Ahead: SK hynix\u2019s Vision for AiMX in the AI Era<\/h3>\n
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The AI Hardware & Edge AI Summit 2024 provided a platform for SK hynix to demonstrate AiMX\u2019s applications in LLMs across data centers and edge devices. As a low-power, high-speed memory solution able to handle large amounts of data, AiMX is set to play a key role in the advancement of LLMs and AI applications.<\/p>\n","protected":false},"excerpt":{"rendered":"

A glimpse of SK hynix\u2019s booth at the AI Hardware & Edge AI Summit 2024 \u00a0 SK hynix unveiled an enhanced Accelerator-in-Memory based Accelerator (AiMX) card at the AI Hardware & Edge AI Summit 2024 held September 9\u201312 in San Jose, California. Organized annually by Kisaco Research, the summit brings together representatives from the AI [\u2026]<\/p>\n","protected":false},"author":23,"featured_media":1814,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_migrated_source_id":15762,"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[5],"tags":[1312,234,968,310,177],"class_list":["post-1823","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-and-ai","tag-ai-hardware-edge-ai-summit","tag-aimx","tag-artificial-intelligence","tag-gddr6-aim","tag-pim"],"acf":[],"_links":{"self":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts\/1823","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/comments?post=1823"}],"version-history":[{"count":3,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts\/1823\/revisions"}],"predecessor-version":[{"id":8422,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts\/1823\/revisions\/8422"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/media\/1814"}],"wp:attachment":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/media?parent=1823"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/categories?post=1823"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/tags?post=1823"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}