The continued scaling of flash memory technology into smaller process nodes, combined with the increased information capacity of each flash cell (i.e, storing more bits per cell), has placed NAND flash memory at the forefront of modern storage technology.

Gear Computers Laptops Amazon just unleashed its Cyber Monday laptop deals and it's dropping prices on MacBooks, gaming PCs, and more Whether you need a basic everyday driver or a full-featured gaming PC, Amazon's Cyber Monday laptop can save you cash. We may earn revenue from the products available on this page and participate in affiliate programs. A laptop is a big investment. Not only do they typically cost a lot of money, but you're committing a machine you'll stare at while you shop, do homework, remote work, game, and pretty much everything else in your online life. Amazon just dropped its Cyber Monday deals on laptops and these are some of the lowest prices we have seen all year.

When you purchase through links in our articles, we may earn a small commission. AI is causing a DRAM apocalypse and it's affecting the whole PC market this holiday season. This year, Black Friday tech shoppers should heed one important message: Don't wait, buy now. Because certain components are skyrocketing in price--and it's expected to get even worse. DRAM prices, for example, have doubled in little more than a month. AI hyperscalers have snapped up whatever they can buy.

Recently, several works have shown that natural modifications of the classical conditional gradient method (aka Frank-Wolfe algorithm) for constrained convex optimization, provably converge with a linear rate when the feasible set is a polytope, and the objective is smooth and strongly-convex. However, all of these results suffer from two significant shortcomings: i) large memory requirement due to the need to store an explicit convex decomposition of the current iterate, and as a consequence, large running-time overhead per iteration ii) the worst case convergence rate depends unfavorably on the dimension In this work we present a new conditional gradient variant and a corresponding analysis that improves on both of the above shortcomings. In particular, both memory and computation overheads are only linear in the dimension, and in addition, in case the optimal solution is sparse, the new convergence rate replaces a factor which is at least linear in the dimension in previous works, with a linear dependence on the number of non-zeros in the optimal solution At the heart of our method, and corresponding analysis, is a novel way to compute decomposition-invariant away-steps. While our theoretical guarantees do not apply to any polytope, they apply to several important structured polytopes that capture central concepts such as paths in graphs, perfect matchings in bipartite graphs, marginal distributions that arise in structured prediction tasks, and more. Our theoretical findings are complemented by empirical evidence that shows that our method delivers state-of-the-art performance.

Zero-knowledge proofs allow verification of computations without revealing private information. However, existing systems require memory proportional to the computation size, which has historically limited use in large-scale applications and on mobile and edge devices. We solve this fundamental bottleneck by developing, to our knowledge, the first proof system with sublinear memory requirements for mainstream cryptographic constructions. Our approach processes computations in blocks using a space-efficient tree algorithm, reducing memory from linear scaling to square-root scaling--from $Θ(T)$ to $O(\sqrt{T} + \log T \log\log T)$ for computation size $T$--while maintaining the same proof generation time through a constant number of streaming passes. For widely-used linear polynomial commitment schemes (KZG/IPA), our method produces identical proofs and verification when using the same parameters and hashing only aggregate commitments into the challenge generation, preserving proof size and security. Hash-based systems also achieve square-root memory scaling though with slightly different proof structures. This advance enables zero-knowledge proofs on everyday devices and makes previously infeasible large computations verifiable, fundamentally democratizing access to privacy-preserving computation. Space-efficient zero knowledge proof systems create opportunities to reshape how trust is established in digital systems--from enabling widespread participation in decentralized networks to making verifiable scientific computing practical at unprecedented scales.

FastText has established itself as a fundamental algorithm for learning word representations, demonstrating exceptional capability in handling out-of-vocabulary words through character-level n-gram embeddings. However, its hash-based bucketing mechanism introduces critical limitations for large-scale industrial deployment: hash collisions cause semantic drift, and memory requirements become prohibitively expensive when dealing with real-world vocabularies containing millions of terms. This paper presents a comprehensive memory optimization framework that fundamentally reimagines FastText's memory management through the integration of double-array trie (DA-trie) structures and mark-compact garbage collection principles. Our approach leverages the linguistic insight that n-grams sharing common prefixes or suffixes exhibit highly correlated embeddings due to co-occurrence patterns in natural language. By systematically identifying and merging semantically similar embeddings based on structural relationships, we achieve compression ratios of 4:1 to 10:1 while maintaining near-perfect embedding quality. The algorithm consists of four sophisticated phases: prefix trie construction with embedding mapping, prefix-based similarity compression, suffix-based similarity compression, and mark-compact memory reorganization. Comprehensive experiments on a 30-million Chinese vocabulary dataset demonstrate memory reduction from over 100GB to approximately 30GB with negligible performance degradation. Our industrial deployment results show significant cost reduction, faster loading times, and improved model reliability through the elimination of hash collision artifacts. Code and experimental implementations are available at: https://github.com/initial-d/me_fasttext

The rapid development of artificial intelligence (AI) over the past few decades has been nourished by advancements in machine learning algorithms, increased computational power, and availability of vast amounts of data[1], which has in turn revolutionized numerous fields including but not limited to medical science and healthcare, information technologies, finance, transportation, and more. This regenerative feedback between AI and its applications leads to a further explosive growth of data and expansion of model scales, which calls for a paradigm shift toward efficient and speedy computing and memory technologies, especially, advanced algorithms and emerging AI hardware enabled by nonvolatile memories[2]. In this aspect, the emerging memory technologies, such as magnetic random-access memories[3], ferroelectric random-access memories[4], resistive random-access memories[5, 6] and phase-change random-access memories[7], have been implemented to accelerate AI computing, for instance, the matrix multiplication[8]. Thanks to their high energy-efficiency, fast speed, long endurance, and versatile functionalities, spin-tronic devices based on spin-orbit torques as one prominent example among emerging memories, have shown great potential in the aspect of hardware-accelerated true random number generation (TRNG)[9-18] besides of the matrix multiplication. For instance, the high quality true random number generators with stable and reconfigurable probability-tunability have been demonstrated using SOT -MTJs [19-21].

Some software bugs mar the experience but overall, AMD's 9070 graphics cards offer such a compelling mix of performance, value, and memory capacity that it's worth accepting those quibbles. Nvidia fumbled the ball with its 549 GeForce RTX 5070, and AMD's new Radeon RX 9070 and 9070 XT are primed to seize advantage. The RTX 5070, hitting store shelves today, is a good 1440p graphics card but a stagnant generational sidegrade at best. Enter the 549 Radeon RX 9070 and 599 Radeon RX 9070 XT, launching tomorrow. Both cards are faster than the RTX 5070, with the 9070 XT going toe-to-toe with the 750 RTX 5070 Ti in many games, and each includes an ample 16GB of VRAM.

UPDATE: Authors answered my questions, I would like to keep my score unchanged and suggest to focus on clarity of the final version. Perhaps, this is the case when I would really be interested in looking at the source code. Originality: the paper borrows the general idea of product keys from the database community, however the application to fast retrieval in neural memory systems seems quite novel to me. Quality: The core ideas of the paper are sound, however more I would appreciate more rigor in both conceptual and experimental comparison with other approaches incorporating memory to Transformer (see e.g. Another suggestion would be to discuss more the issue of potential non-uniformity of the query distribution, which indeed seems to be quite relevant.

Advancements in technology and reduction in it's cost have led to a substantial growth in the quality & quantity of imagery captured by Earth Observation (EO) satellites. This has presented a challenge to the efficacy of the traditional workflow of transmitting this imagery to Earth for processing. An approach to addressing this issue is to use pre-trained artificial intelligence models to process images on-board the satellite, but this is difficult given the constraints within a satellite's environment. This paper provides an up-to-date and thorough review of research related to image processing on-board Earth observation satellites. The significant constraints are detailed along with the latest strategies to mitigate them.