- #RANDOM ACCESS MEMORY IMAGE GENERATOR#
- #RANDOM ACCESS MEMORY IMAGE DRIVERS#
- #RANDOM ACCESS MEMORY IMAGE SOFTWARE#
c, MVM output distribution from a CNN layer and from an LSTM layer (weights normalized to the same range).
b, Voltage-mode sensing employed by NeuRRAM can activate all the rows and all the columns in a single cycle, enabling higher parallelism. Weights are encoded as the differential conductance between two RRAM cells on adjacent rows (G+ and G-).Ī, Conventional current-mode-sensing scheme needs to activate a small fraction of total N rows each cycle to limit total current I SL and time-multiplex ADCs across multiple columns to amortize ADC area, thus limiting its computational parallelism. h, Differential input and differential output schemes used to implement real-valued weights during forwards and backwards MVMs. g, The TNSA can be dynamically configured for MVM in forwards, backwards or recurrent directions. The neuron connects to 1 of the 16 BLs and 1 of the 16 SLs that pass through the corelet, and can use a BL and a SL for both its input and output. f, Each corelet contains 16 × 16 RRAMs and 1 neuron. Each neuron integrates inputs from 256 RRAMs connecting to the same horizontal BL or vertical SL. e, The architecture of a TNSA consists of 16 × 16 corelets with interleaving RRAM weights and CMOS neurons.
#RANDOM ACCESS MEMORY IMAGE DRIVERS#
During the MVM input stage, the drivers convert register inputs (REG) and PRNG inputs (PRN) to analogue voltages and send them to TNSA during the MVM output stage, the drivers pass digital outputs from neurons back to registers through REG.
#RANDOM ACCESS MEMORY IMAGE GENERATOR#
A core consists of a TNSA, drivers for BLs, WLs, and SLs, registers that store MVM inputs and outputs, a LFSR pseudo-random number generator (PRNG), and a controller. c, A cross-sectional transmission electron microscopy image showing the layer stack of the monolithically integrated RRAM and CMOS. b, Zoomed-in chip micrograph on a single CIM core.
#RANDOM ACCESS MEMORY IMAGE SOFTWARE#
Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM-a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST 18 and 85.7 percent on CIFAR-10 19 image classification, 84.7-percent accuracy on Google speech command recognition 20, and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task.Ī, Multi-core architecture of the NeuRRAM chip, and various ways, labelled (1) to (6), to map neural-network layers onto CIM cores. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware 6-17, it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) 1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory 2-5. Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware.
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