New Memory Architecture For Local Differential Privacy in Hardware


A technical paper titled "Two Birds with One Stone: Differential Privacy by Low-power SRAM Memory" was published by researchers at North Carolina State University, University of South Alabama, and University of Tennessee. Abstract "The software-based implementation of differential privacy mechanisms has been shown to be neither friendly for lightweight devices nor secure against side-channe... » read more

SRAM Scaling Issues, And What Comes Next


The inability of SRAM to scale has challenged power and performance goals forcing the design ecosystem to come up with strategies that range from hardware innovations to re-thinking design layouts. At the same time, despite the age of its initial design and its current scaling limitations, SRAM has become the workhorse memory for AI. SRAM, and its slightly younger cousin DRAM, have always co... » read more

CiM Integration For ML Inference Acceleration


A technical paper titled “WWW: What, When, Where to Compute-in-Memory” was published by researchers at Purdue University. Abstract: "Compute-in-memory (CiM) has emerged as a compelling solution to alleviate high data movement costs in von Neumann machines. CiM can perform massively parallel general matrix multiplication (GEMM) operations in memory, the dominant computation in Machine Lear... » read more

SRAM’s Role In Emerging Memories


Experts at the Table — Part 3: Semiconductor Engineering sat down to talk about AI, the latest issues in SRAM, and the potential impact of new types of memory, with Tony Chan Carusone, CTO at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part one of this conversation can be ... » read more

Mixed SRAM And eDRAM Cell For Area And Energy-Efficient On-Chip AI Memory (Yale Univ.)


A new technical paper titled "MCAIMem: a Mixed SRAM and eDRAM Cell for Area and Energy-efficient on-chip AI Memory" was published by researchers at Yale University. Abstract: "AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies... » read more

The Uncertain Future Of In-Memory Compute


Experts at the Table — Part 2: Semiconductor Engineering sat down to talk about AI and the latest issues in SRAM with Tony Chan Carusone, chief technology officer at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part one of this conversation can be found here and part 3 is h... » read more

DRAM Choices Are Suddenly Much More Complicated


Chipmakers are beginning to incorporate multiple types and flavors of DRAM in the same advanced package, setting the stage for increasingly distributed memory but significantly more complex designs. Despite years of predictions that DRAM would be replaced by other types of memory, it remains an essential component in nearly all computing. Rather than fading away, its footprint is increasing,... » read more

SRAM In AI: The Future Of Memory


Experts at the Table — Part 1: Semiconductor Engineering sat down to talk about AI and the latest issues in SRAM with Tony Chan Carusone, CTO at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part two of this conversation can be found here and part three is here. [L-R]: ... » read more

SRAM-Based IMC For Cryogenic CMOS Using Commercial 5 nm FinFETs


A technical paper titled “Cryogenic In-Memory Computing for Quantum Processors Using Commercial 5-nm FinFETs” was published by researchers at University of Stuttgart, Indian Institute of Technology Kanpur, University of California Berkeley, and Technical University of Munich. Abstract: "Cryogenic CMOS circuits that efficiently connect the classical domain with the quantum world are the co... » read more

An Energy-Efficient 10T SRAM In-Memory Computing Macro Architecture For AI Edge Processor


A technical paper titled “An energy-efficient 10T SRAM in-memory computing macro for artificial intelligence edge processor” was published by researchers at Atal Bihari Vajpayee-Indian Institute of Information Technology and Management (ABV-IIITM). Abstract: "In-Memory Computing (IMC) is emerging as a new paradigm to address the von-Neumann bottleneck (VNB) in data-intensive applications.... » read more

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