74 citations · 200 across the 17 of their papers we have counts for
6 papers · 1 filter
THOR -- A Neuromorphic Processor with 7.29G TSOP/mmJs Energy-Throughput Efficiency
Mayank Senapati, Manil Dev Gomony, Sherif Eissa +2
Neuromorphic computing using biologically inspired Spiking Neural Networks (SNNs) is a promising solution to meet Energy-Throughput (ET) efficiency needed for edge computing device…
BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC
Maarten Molendijk, Floran de Putter, Manil Gomony +2
Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut do…
Low- and Mixed-Precision Inference Accelerators
Maarten Molendijk, Floran de Putter, Henk Corporaal
With the surging popularity of edge computing, the need to efficiently perform neural network inference on battery-constrained IoT devices has greatly increased. While algorithmic…
How to train accurate BNNs for embedded systems?
Floran de Putter, Henk Corporaal
A key enabler of deploying convolutional neural networks on resource-constrained embedded systems is the binary neural network (BNN). BNNs save on memory and simplify computation b…
Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors
Wei Sun, Ang Li, Tong Geng +2
Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users ha…
Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
Gagandeep Singh, Rakesh Nadig, Jisung Park +7
Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques t…