• 대한전자공학회논문지SD
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• 제48권2호
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• pp.55-60
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• 2011

그동안 신경망칩의 설계에는 주로 아날로그 Maximum Selector (MS) 회로를 사용하였다. 그러나 집적도가 높아질수록 아날로그 MS회로는 신호의 해상도(Resolution)을 높이는데 어려움이 있다. 반면 디지털 MS 회로는 높은 해상도를 얻기는 쉬우나 속도가 느린 단점이 있었다. 본 논문에서는 신경망칩의 디지털화에 사용하기 위한 MSIT(Maximum Selector with Internal Trigger-Signal) 라는 고속의 디지털 MS회로를 개발하였다. MSIT는 제어신호 발생기를 내장하여 안정적인 동작을 확보하고, 불필요한 대기시간을 없애도록 이를 최적화 함으로써 높은 속도를 얻을 수 있다. 1.2V-$0.13{\mu}m$ 프로세스의 모델파라메터를 사용하여 32 개의 10 비트 데이터에 대하여 시뮬레이션을 수행한 결과 3.4ns의 응답시간을 얻을 수 있었다. 이는 동급의 해상도를 갖는 아날로그 MS회로 보다 훨씬 빠른 속도로써, MSIT와 같은 디지털 MS 회로가 아날로그 MS회로에 비하여 높은 해상도와 빠른 속도를 구현할 수 있음을 보여준다.

• Journal of Communications and Networks
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• 제18권2호
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• pp.218-226
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• 2016

Coordination among users is an inevitable but time-consuming operation in wireless networks. It severely limit the system performance when the data rate is high. We present FC-MAC, a novel MAC protocol that can complete a contention within one contention slot over a joint frequency-code domain. When a node takes part in the contention, it generates randomly a contention vector (CV), which is a binary sequence of length equal to the number of available orthogonal frequency division multiplexing (OFDM) subcarriers. In FC-MAC, different user is assigned with a distinct signature (i.e., PN sequence). A node sends the signature at specific subcarriers and uses the sequence of the ON/OFF states of all subcarriers to indicate the chosen CV. Meanwhile, every node uses the redundant antennas to detect the CVs of other nodes. The node with the minimum CV becomes the winner. The experimental results show that, the collision probability of FC-MAC is as low as 0.05% when the network has 100 nodes. In comparison with IEEE 802.11, contention time is reduced by 50-80% and the throughput gain is up to 200%.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.150-190
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• 2024

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.