Sram
PulseAugur coverage of Sram — every cluster mentioning Sram across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Nagare Labs unveils Z2RAM for AI chips, promising SRAM speed at DRAM density
Nagare Labs, a startup backed by Pat Gelsinger, has developed Z2RAM, a new memory technology that aims to provide SRAM speeds with DRAM density. This capacitorless design, detailed in published papers, has demonstrated …
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New compute-in-memory engine boosts CNN inference efficiency
Researchers have developed SLICE-CiM, a novel control engine for SRAM-based compute-in-memory (CIM) macros designed to enhance energy efficiency in CNN inference. The system incorporates four peripheral blocks, includin…
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AI inference splits: SRAM for decode, HBM for training
AI inference is diverging into two distinct paths based on hardware requirements. Latency-sensitive decode operations are moving to SRAM-based chips that eschew High Bandwidth Memory (HBM), exemplified by NVIDIA's Groq …
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Samsung custom HBM4 shrinks interface, frees GPU die space · 3 sources tracked
Samsung is developing custom High Bandwidth Memory (HBM) solutions that significantly reduce the physical footprint and improve efficiency compared to standard HBM4. These custom designs replace wide PHY interfaces with…
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Volatile vs. Non-Volatile Memory: Understanding the Core Differences
Volatile memory, such as DRAM and SRAM, requires a constant power supply to retain data, making it suitable for active processing and CPU caches. In contrast, non-volatile memory, like NAND flash used in SSDs and NOR fl…
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Cerebras upgrades CS-4 with new I/O module for disaggregated inference
Cerebras has introduced a new I/O module for its CS-4 wafer-scale system, addressing the historical bottleneck of off-chip bandwidth. This upgrade doubles the off-wafer I/O speed to 2.4Tb/s and includes a field-upgradea…
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AI frameworks advance silicon photonics design and energy-harvesting AI concepts
Researchers have developed PICasso, an AI-enabled framework that uses natural language to design, verify, and optimize silicon photonic integrated circuits. This framework couples LLMs with domain-specific knowledge and…
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LLM agents create adaptive hardware Trojans to test detector weaknesses
Researchers have developed TrojanGYM, a novel framework that utilizes multiple large language models to create adaptive hardware Trojans. These Trojans are designed to bypass existing learning-based detectors by generat…
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AMD acquires Taalas to push specialized AI inference chips
AMD has acquired Taalas, a Canadian AI inference chip company, to integrate its specialized hardware into AMD's accelerator roadmap. Taalas focuses on creating chips optimized for specific AI models, aiming to reduce in…
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AI models etched into silicon for faster inference, bypassing memory bottlenecks
A new trend is emerging where AI models are being etched directly into silicon, a process that permanently embeds model weights as physical transistors. This approach, exemplified by companies like Taalas and previously…
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SK Hynix, SanDisk unveil High Bandwidth Flash for AI inference memory wall
SK Hynix and SanDisk have collaborated to develop High Bandwidth Flash (HBF), a new memory tier designed to address the memory wall challenges in AI inference. HBF places large-capacity NAND flash memory close to variou…
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MCP, eMMC, and eMCP: Understanding Integrated Memory Technologies
This article delves into the distinctions between Multi-Chip Package (MCP), embedded MultiMediaCard (eMMC), and embedded Multi-Chip Package (eMCP) technologies. MCP is a packaging method that integrates multiple chips l…
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DJI-spinoff Amflow e-bikes surpass 1B yuan revenue, challenging market leaders
Amflow, an e-bike company spun out of DJI, has achieved over 1 billion yuan in revenue, selling high-end electric mountain bikes priced significantly higher than typical market offerings. The company, along with its sis…
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New optical receiver updates robot AI via light, cutting energy use
Researchers have developed a novel optical receiver that can directly alter a robot's AI model parameters using light. This technology aims to overcome the significant memory and energy demands of current AI systems by …
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NumPy implementation of Flash Attention demonstrates significant memory savings
This article details the implementation of Flash Attention from first principles using NumPy. Flash Attention optimizes transformer models by avoiding the materialization of large N×N attention score matrices, which con…
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New UEP codec slashes AI inference memory costs by up to 62.5%
Researchers have developed a new method for protecting memory in AI inference by analyzing bit-position fault sensitivity in various models and floating-point formats. They found that certain lower-order bits have minim…
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New ThRIve method boosts CNN inference robustness in PIM architectures
Researchers have developed ThRIve, a novel training methodology designed to enhance the thermal robustness of Convolutional Neural Network (CNN) inference on Processing-In-Memory (PIM) architectures. This approach utili…
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3D Gaussian renderer implemented on AI accelerator with on-chip SRAM
Researchers have developed the first implementation of a 3D Gaussian renderer specifically for an Intelligence Processing Unit (IPU), a type of AI accelerator. This system utilizes 1,472 independent tiles with on-chip S…
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36Kr: Jiangling Motors sales up 0.04% in June; Beijing Junzheng sees DRAM price hikes
36Kr reported that Jiangling Motors sold 35,742 vehicles in June, a slight year-over-year increase of 0.04%. Year-to-date sales reached 189,000 units, up 9.46%. Separately, Beijing Junzheng noted significant price incre…
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Beijing Junzheng forecasts continued DRAM price hikes amid tight supply
Beijing Junzheng anticipates further price increases for DRAM in the third quarter, with a possibility of adjustments in the fourth quarter due to continued tight supply. The company also noted a price increase for some…