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Rethinking AI hardware with tiny vibrating beams

Cornell researchers have developed a new type of computing device that stores information electrically but reads it through tiny mechanical motion, an unusual approach that could open a path toward ...
IEEE

A 2941-TOPS/W Charge-Domain 10T SRAM Compute-in-Memory for Ternary Neural Network

Abstract: In this paper, we present a 10T SRAM compute-in memory (CiM) macro to process the multiplication-accumulation (MAC) operations between ternary-inputs and binary-weights. In the proposed 10T ...
The Next Platform

Tensordyne Converts AI Matrix Math To Logs To Crank Up Inference Oomph

Transformations are the key to such codes, and they rely on math that predates computing as we know it by centuries. There ...
IEEE

Energy-Efficient Low-Latency Signed Multiplier for FPGA-Based Hardware Accelerators

Abstract: Multiplication is one of the most extensively used arithmetic operations in a wide range of applications, such as multimedia processing and artificial neural networks. For such applications, ...
LinkedIn

Self Attention is Just Matrix Multiplication

𝗦𝗲𝗹𝗳 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝘀𝗼𝗻 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗰𝗮𝗻 ...
GitHub

LLM.int8() - 8-bit Matrix Multiplication for Transformers at Scale - 2022 (2208.07339v2).pdf

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GitHub

Quantization and Synthesis (Device Specific Code Generation) for ADI's MAX78000 and MAX78002 Edge AI Devices

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