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Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency
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๐บ๐ธ ๋ฏธ๊ตญ ยท "EFFICIENCY" ยท ์ด 30๊ฑด
ํํฐ ๋ณด๊ธฐํ์ฌ ์ง์
50.0
0 = ๋ถ์ ์ฐ์ธ
50 = ์ค๋ฆฝ
100 = ๊ธ์ ์ฐ์ธ
์ต๊ทผ 7์ผ ๊ธฐ์ค 11,263๊ฑด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ๋ด์ค ์ฌ๋ฆฌ์ง์๋ 50.0(๊ท ํ)์ ๋๋ค. ๊ธ์ 1๊ฑด(0.0%)ยท์ค๋ฆฝ 11,261๊ฑด(100.0%)ยท๋ถ์ 1๊ฑด(0.0%)์ด๋ฉฐ, ์ค๋ฆฝ ๋น์ค์ด ๋๋ ทํ๊ฒ ๋์ต๋๋ค. ์ฑํฅ ์ง์๋ ์ข ํฉ 18.6(์ค๋ ๊ท ํ)์ ๋๋ค.
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This is the third whistleblower to come forward about alleged illegal activity by Trumpโs Department of Government Efficiency.
At the start of 2026, oil traders were preparing for a glut. Supply growth was expected to outpace demand growth. OPEC+ was gradually returning barrels to the market. U.S. production remained near record highs. Economic growth was slowing, while electrification and efficiency gains were expected to temper consumption growth. The consensus view was simple: the world was heading into a period of excess supply. Six months later, that narrative never came true. Not because the world suddenly ran out of oil. In fact, many major producers are pumpingโฆ
What happens when your home starts thinking for itself? Agentic AI is turning energy systems into autonomous partners that optimize cost, efficiency and resilience.
Katherine Mangu-Ward appears on the left. Peter Suderman appears on the right. An image of Zohran Mamdani in front of a coin with a US dollar sign on its appears in the center square. Bold text across the top of the screen reads "DOGE FOR SOCIALISTS?"
Americans will no longer be able to get cash back for making the switch to electric appliances under a Trump administration change. The Energy Department recently issued guidance reinterpreting a program in the 2022 Inflation Reduction Act that set up a rebate program for people who make high-efficiency electric purchases for their homes. The program...
Amazon and Uber recalibrate AI usage as tokenmaxxing, a trend of excessive AI use, sparks debate on productivity and cost efficiency.
Over the last two decades, solar panels have fallen in price while efficiency has increased. Greater uptake and high levels of investment in research and development have led to vast improvements in solar power technology. As panel prices fall and governments worldwide look to diversify their energy mix and cut emissions, several developers are now launching mega-projects to meet the growing demand. Most major solar projects developed in recent years provide hundreds of megawatts of clean power. However, as operators become more ambitious and governmentsโฆ
AI is using a lot of electricity. Energy leaders explain how efficiency, advanced cooling, and flexible data centers can help meet rising electricity demand.
Spurs-Thunder Game 7 betting card features San Antonio +3.5 as the top pick, citing superior net efficiency and Gilgeous-Alexander's shooting woes.
NYC Mayor Zohran Mamdani introduced COGE, a commission to improve city government efficiency, sparking praise from Bezos and DOGE comparisons.
NYC Mayor Zohran Mamdani wants to improve his government's efficiency with a new agency. Just don't call it DOGE.
Treasury Secretary Scott Bessent will deliver a speech at the Reagan National Economic Forum on Friday warning that decades of U.S. industrial policy have left the country strategically vulnerable. During a talk titled "While America Slept," Bessent will argue that policymakers have sought efficiency over resilience, hollowing out productive capacity in semiconductors, rare earths, medicines...
Outages disable furnaces, pumps, and medical devices. Consumer Reports tested six standby generators on output, efficiency, and noise to find the best
Mayor Zohran Mamdani unveiled a "Commission on Government Efficiency" Thursday -- but it's got nothing to do with cutting waste and fraud despite its DOGE-like name.
New York City Mayor Zohran Mamdani takes a page from Elon Musk's DOGE playbook by launching his own city Commission on Government Efficiency.
New York City Mayor Zohran Mamdani (D) announced the appointment of a new commission on Thursday focused on improving the effectiveness of the city's government. The newly-unveiled Commission on Government Efficiency (COGE) drew immediate comparisons to the controversial Department of Government Efficiency (DOGE) championed by President Trump and Elon Musk, which cut hundreds of thousands...
New York City Mayor Zohran Mamdani announced a plan to recreate a Big Apple version of the Trump administrationโs Department of Government Efficiency, called the Commission on Government Efficiency. New Yorkโs COGE will examine the cityโs charter and figure out ways for Mamdaniโs administration to cut costs, improve efficiency, and modernize the cityโs government, the [โฆ]
Mayor Zohran Mamdani is launching his own government efficiency effort to cut red tape and reduce unnecessary spending in NYC.
I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, Iโve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in todayโs most advanced chips is occupied by blocks that arenโt made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations. Throughout my career, Iโve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset. Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designersโbut if youโre coming from an academic background as I did, there are a few things youโll need to know. Different goals lead to different strategies The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule. This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirementโespecially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions. โAcademia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.โ This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical โfinsโ of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.โs University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics. What the industry-academia split means in practice Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startupโs viability. Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chipโs ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing. The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative. In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocksโsuch as processor cores, memory interfaces, and security enginesโfrom highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems. Design scope, verification, and time horizons With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield. In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block. Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea. At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we werenโt required to mention this when publishing the results. In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face. Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain. In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape. This article appears in the June 2026 print issue.