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ICCAD Paper Accepted

July 30th, 2022

Congratulations!  Our paper titled “Squeezing Accumulators in Binary Neural Networks for Extremely Resource-Constrained Applications,” authored by Azat and Jaewoo has been accepted to the 41st International Conference on Computer-Aided Design (ICCAD 2022), which is held in San Diego, California, in October 30 – November 3.

Unlike the previous papers trying to reduce the multiplication overhead of neural network hardware, this paper asks a different question that is, in binarized neural networks and extremely low-precision quantized neural networks, what is the real bottleneck in hardware implementation?  It turns out that accumulators now take a lion’s share in terms of not only area but more power dissipation, and we propose a novel method to minimize accumulator overhead.


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