In computational neuroscience, SUPS (for Synaptic Updates Per Second) or formerly CUPS (Connections Updates Per Second) is a measure of a neuronal network performance, useful in fields of neuroscience, cognitive science, artificial intelligence, and computer science.


For a processor or computer designed to simulate a neural network SUPS is measured as the product of simulated neurons and average connectivity (synapses) per neuron per second:

Depending on the type of simulation it is usually equal to the total number of synapses simulated.

In an "asynchronous" dynamic simulation if a neuron spikes at Hz, the average rate of synaptic updates provoked by the activity of that neuron is . In a synchronous simulation with step the number of synaptic updates per second would be . As has to be chosen much smaller than the average interval between two successive afferent spikes, which implies , giving an average of synaptic updates equal to . Therefore, spike-driven synaptic dynamics leads to a linear scaling of computational complexity O(N) per neuron, compared with the O(N2) in the "synchronous" case.[1]


Developed in the 1980s Adaptive Solutions' CNAPS-1064 Digital Parallel Processor chip is a full neural network (NNW). It was designed as a coprocessor to a host and has 64 sub-processors arranged in a 1D array and operating in a SIMD mode. Each sub-processor can emulate one or more neurons and multiple chips can be grouped together. At 25 MHz it is capable of 1.28 GMAC.[2]

After the presentation of the RN-100 (12 MHz) single neuron chip at Seattle 1991 Ricoh developed the multi-neuron chip RN-200. It had 16 neurons and 16 synapses per neuron. The chip has on-chip learning ability using a proprietary backdrop algorithm. It came in a 257-pin PGA encapsulation and drew 3.0 W at a maximum. It was capable of 3 GCPS (1 GCPS at 32 MHz). [3]

In 1991-97, Siemens developed the MA-16 chip, SYNAPSE-1 and SYNAPSE-3 Neurocomputer. The MA-16 was a fast matrix-matrix multiplier that can be combined to form systolic arrays. It could process 4 patterns of 16 elements each (16-bit), with 16 neuron values (16-bit) at a rate of 800 MMAC or 400 MCPS at 50 MHz. The SYNAPSE3-PC PCI card contained 2 MA-16 with a peak performance of 2560 MOPS (1.28 GMAC); 7160 MOPS (3.58 GMAC) when using three boards.[4]

In 2013, the K computer was used to simulate a neural network of 1.73 billion neurons with a total of 10.4 trillion synapses (1% of the human brain). The simulation ran for 40 minutes to simulate 1 s of brain activity at a normal activity level (4.4 on average). The simulation required 1 Petabyte of storage.[5]

See also


  1. ^ Maurizio Mattia; Paolo Del Giudice (1998). Asynchronous simulation of large networks of spiking neurons and dynamical synapses. Proceedings of the 8th International Conference on Artificial Neural Networks. Perspectives in Neural Computing. pp. 1045–1050. CiteSeerX doi:10.1007/978-1-4471-1599-1_164. ISBN 978-3-540-76263-8.
  2. ^ Real-Time Computing: Implications for General Microprocessors Chip Weems, Steve Dropsho
  3. ^ L. Almeida; Luis B. Almeida; S. Boverie (2003). Intelligent Components and Instruments For Control Applications 2003 (SICICA 2003). ISBN 9780080440101.
  4. ^ Neural Network Hardware Clark S. Lindsey, Bruce Denby, Thomas Lindblad, 1998
  5. ^ Fujitsu supercomputer simulates 1 second of brain activity Tim Hornyak, CNET, August 5, 2013