DRAMSpec: A High-Level DRAM Timing, Power and Area Exploration Tool

Weis, Christian; Mutaal, Abdul; Naji, Omar; Jung, Matthias; Hansson, Andreas; Wehn, Norbert

doi:10.1007/s10766-016-0473-y

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…The NMC system is simulated with Ramulator-PIM [26] once, since the results do not vary in different runs. Power and time parameters for HMC are derived from [22], [27] and fed to the NMC simulator. As a benchmark, we selected a set of application from Polybench since it consists of simple mathematical operations extensively used in modern applications, and are also commonly used in NMC related work [28].…”

Section: Correct Predictions Total Number Of Predictionsmentioning

confidence: 99%

NMPO: Near-Memory Computing Profiling and Offloading

Corda

Kumaraswamy

Awan

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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Section: Correct Predictions Total Number Of Predictionsmentioning

confidence: 99%

NMPO: Near-Memory Computing Profiling and Offloading

Corda

Kumaraswamy

Awan

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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“…For eight layers, this adds up to 32Gb, thus allowing 32 H-Cubes. The timing, current, and area values of the presented custom 3D DRAM for the 28 nm process with the TSV pitch of 40 μm are extracted from DRAMSpec [20,21], and the exact area dimension of each H-Cube is shown in Fig. 8.…”

Section: Dimensioning the Bcumentioning

confidence: 99%

eBrainII: a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex

Stathis

Sudarshan

Yang

et al. 2020

J Sign Process Syst

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The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems. The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment.

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