Hossein Askari
I am a reserach engineer at Meta. I work on developing new methods for efficient AI models. I recieved my PhD from
Ecole Polytechnique Montreal in electrical engineering.
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Recent Publications
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QGen: On the Ability to Generalize in Quantization Aware Training
In this project, we investigate the generalization properties of quantized neural networks. We develop a theoretical model
demonstrating how quantization functions as regularization and derive an approximate bound for generalization conditioned
on quantization noise. To measure generalization, we used proxy measures such as sharpness and validated our hypothesis
through experiments on over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets, covering both convolutional
and transformer-based models.
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RISC-V Barrel Processor for Deep Neural Network Acceleration
ISCAS 2021
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Based on the architecture proposed in our FCCM 2020 paper, we built a RISC-V core that is connected to a neural network accelerator capable of performing Matrix Vector product. We used this system to compute a GEMV operation with an input matrix size of 8 by 128 and a weight matrix size of 128 by 128 with two-bit precision in only 16 clock cycles.
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RISC-V Barrel Processor for Accelerator Control
FCCM 2020
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In this paper we designed a Barrel RISC-V processor. We used 8 harts (hardware threads) to control 8 Matrix Vector Units for a Deep Neural Network application. We have implemented our design on a Xilinx Ultrascale FPGA. Our 8-hart barrel processor runs at 350 MHz with CPI of 1 and consumes 0.287W.
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Talks/Workshops
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Hardware Aware Acceleration For Deep Neural Network
CMC Workshop: Accelerating AI - Challenges and Opportunities in Cloud and Edge Computing, Mar 6th, 2020
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In this presentation, I talked about how to accelerate computation in Deep Neural Networks. Specifically, I talked about Quantization. Quantization in Deep Learning is a technique to reduce power, memory and computation time of deep neural networks. I talked about how one can improve the performance of a DNN using both software and hardware solutions.
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Workshop on New Methods on Designing Digital Systems
CMC Workshop: Accelerating AI - Challenges and Opportunities in Cloud and Edge Computing, Mar 6th, 2020
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In this workshop, I reviewed the most popular open source tools for design and simulation of digital systems. The attendants got a chance to use these tools and developed a simple circuit to calculate GCD. In the second part of the workshop, I talked about RISC-V and Chisel. At the end of the workshop, the attendants got a chance to use chisel to designa and simulate a 3-stage pipelined RISC-V core.
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