Evolving challenges and strategies in AI/ML model deployment and hardware optimization have a big impact on NPU architectures ...
Abstract: Variable-rate coding is challenging but indispensable for learned image compression (LIC) that is in nature characterized by nonlinear transform coding (NTC). Existing methods for ...
Dr. Witt is the author of “The Radical Fund: How a Band of Visionaries and a Million Dollars Upended America.” In a year when the United States seemed more split than ever, Americans united in one way ...
The reason why large language models are called ‘large’ is not because of how smart they are, but as a factor of their sheer size in bytes. At billions of parameters at four bytes each, they pose a ...
I am encountering an issue while attempting to quantize the Qwen2.5-Coder-14B model using the auto-gptq library. The quantization process fails with a torch.linalg.cholesky error, indicating that the ...
Abstract: Directly affecting both error performance and complexity, quantization is critical for MMSE MIMO detection. However, naively pruning quantization levels is ...
School of Electrical and Computer Engineering, Cornell Tech, New York, NY, United States Spiking neural networks (SNNs) have received increasing attention due to their high biological plausibility and ...
Deep neural network training can be sped up by Fully Quantised Training (FQT), which transforms activations, weights, and gradients into lower precision formats. The training procedure is more ...
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