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MIT’s heat-powered silicon chips hit 99% accuracy in math tests

Engineers at MIT have turned one of computing’s biggest headaches, waste heat, into the main act. By sculpting “dust-sized” silicon structures that steer heat as precisely as electrical current, they ...
Tech Xplore

Tiny silicon structures compute with heat, achieving 99% accurate matrix multiplication

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...
IEEE

Detection Problems in the Spiked Random Matrix Models

Abstract: We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first show that the ...
Le Lézard

MicroCloud Hologram Inc. Utilizes FPGA to Accelerate Tensor Network Computing to Achieve Quantum Spin Models

MicroCloud Hologram Inc. , ("HOLO" or the "Company"), a technology service provider, proposed an innovative hardware acceleration technology that converts the quantum tensor network algorithm into ...
IEEE

ScanNow: A Scan Window-Based Sparse Matrix Multiplication Accelerator Design

Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...