Ultra-Low Power CiM Design For Practical Edge Scenarios

A technical paper titled “Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET” was published by researchers at Zhejiang University, University of Notre Dame, Technical University of Munich, Munich Institute of Robotics and Machine Intelligence, and the Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province. Abstract: "Compute... » read more

3D Integration Supports CIM Versatility And Accuracy

Compute-in-memory (CIM) is gaining attention due to its efficiency in limiting the movement of massive volumes of data, but it's not perfect. CIM modules can help reduce the cost of computation for AI workloads, and they can learn from the highly efficient approaches taken by biological brains. When it comes to versatility, scalability, and accuracy, however, significant tradeoffs are requir... » 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

Modeling Compute In Memory With Biological Efficiency

The growing popularity of generative AI, which uses natural language to help users make sense of unstructured data, is forcing sweeping changes in how compute resources are designed and deployed. In a panel discussion on artificial intelligence at last week’s IEEE Electron Device Meeting, IBM’s Nicole Saulnier described it as a major breakthrough that should allow AI tools to assist huma... » read more

Increasing AI Energy Efficiency With Compute In Memory

Skyrocketing AI compute workloads and fixed power budgets are forcing chip and system architects to take a much harder look at compute in memory (CIM), which until recently was considered little more than a science project. CIM solves two problems. First, it takes more energy to move data back and forth between memory and processor than to actually process it. And second, there is so much da... » read more

CMOS-Based HW Topology For Single-Cycle In-Memory XOR/XNOR Operations

A technical paper titled “CMOS-based Single-Cycle In-Memory XOR/XNOR” was published by researchers at University of Tennessee, University of Virginia, and Oak Ridge National Laboratory (ORNL). Abstract: "Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a ... » read more

Research Bits: November 6

Fast superatomic semiconductor Researchers from Columbia University created a fast and efficient superatomic semiconductor material based on rhenium called Re6Se8Cl2. Rather than scattering when they come into contact with phonons, excitons in Re6Se8Cl2 bind with phonons to create new quasiparticles called acoustic exciton-polarons. Although polarons are found in many materials, those in Re6Se... » read more

Design Optimization Of Split-Gate NOR Flash For Compute-In-Memory

A technical paper titled “Design Strategies of 40 nm Split-Gate NOR Flash Memory Device for Low-Power Compute-in-Memory Applications” was published by researchers at Seoul National University of Science and Technology and University of Seoul. Abstract: "The existing von Neumann architecture for artificial intelligence (AI) computations suffers from excessive power consumption and memo... » read more

Architectural Considerations For Compute-In-Memory In AI Inference

Can Compute-in-Memory (CIM) bring new benefits to AI (Artificial Intelligence) inference? CIM is not an AI solution; rather, it is a memory management solution. CIM could bring advantages to AI processing by speeding up the multiplication operation at the heart of AI model execution. To read more, click here. » read more

Can Compute-In-Memory Bring New Benefits To Artificial Intelligence Inference?

Compute-in-memory (CIM) is not necessarily an Artificial Intelligence (AI) solution; rather, it is a memory management solution. CIM could bring advantages to AI processing by speeding up the multiplication operation at the heart of AI model execution. However, for that to be successful, an AI processing system would need to be explicitly architected to use CIM. The change would entail a shift ... » read more

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