ROLLS - INI

ROLLS is a reconfigurable neuromorphic chip with 256 silicon neurons and 128K plastic synapses that implements spike-timing learning rules. It allows emulation of neural systems with adaptive behaviors using analog neuron/synapse circuits with added digital configuration logic for flexibility.

The ROLLS chip implements key requirements for online learning and adaptive behavior in neuromorphic systems, with highly flexible digital configuration options.

Overview

• Presented as a “Reconfigurable On-line Learning Spiking Neuromorphic Processor”
• 256 silicon neurons, 128K plastic synapses, 256K programmable synapses
• Mixed-signal analog/digital implementation
• 180nm CMOS process, 51.4 mm2 die area
• Modeling of neural systems that adapt in real-time through on-chip learning

Silicon Neurons

• Implement adaptive exponential Integrate-and-Fire (I&F) model
• Exhibit various spiking behaviors (frequency adaptation, refractory period, threshold adaptation, bursting)
• Highly configurable through 13 tunable bias parameters
• Low mismatch between neurons (9.4% avg)
• On-chip learning circuits to evaluate spike-timing-based plasticity rules

Synapses

• Two core arrays:
  • Long-term plasticity (LTP) synapses with bi-stable weights using drift-diffusion mechanism
  • Short-term plasticity (STP) synapses with 4-level programmable weights and short-term depression
• LTP array models NMDA receptors with longer time constants
• STP array models AMPA/GABA receptors with shorter time constants
• Sparse digital logic for memory and configuration in every synapse
• Digital configuration of synapse types and connectivity

Architecture

• Virtual “diffuser” synapses to combine signals from groups of synapses
• Synapse demultiplexing to allow flexible neuron-synapse mapping
• Asynchronous peripheral digital circuits for input/output
• Bias generator for tuning analog parameters
• ADC readout circuit for monitoring internal signals

Applications

• Hardware attractor networks to model working memory and decision making
• Real-time image classification demonstration with spiking vision sensor

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