1k citations · 1.2k across the 9 of their papers we have counts for
10 papers
NITI: Training Integer Neural Networks Using Integer-only Arithmetic
Maolin Wang, Seyedramin Rasoulinezhad, Philip H. W. Leong +1
While integer arithmetic has been widely adopted for improved performance in deep quantized neural network inference, training remains a task primarily executed using floating poin…
LUXOR: An FPGA Logic Cell Architecture for Efficient Compressor Tree Implementations
SeyedRamin Rasoulinezhad, Siddhartha, Hao Zhou +3
We propose two tiers of modifications to FPGA logic cell architecture to deliver a variety of performance and utilization benefits with only minor area overheads. In the irst tier,…
MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency
Seyedramin Rasoulinezhad, Sean Fox, Hao Zhou +3
Binarized neural networks (BNNs) have shown exciting potential for utilising neural networks in embedded implementations where area, energy and latency constraints are paramount. W…
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers
Julian Faraone, Martin Kumm, Martin Hardieck +4
Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potenti…
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
Jiang Su, Nicholas J. Fraser, Giulio Gambardella +5
Modern CNN are typically based on floating point linear algebra based implementations. Recently, reduced precision NN have been gaining popularity as they require significantly les…
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
Julian Faraone, Nicholas Fraser, Michaela Blott +1
Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce t…