An instruction to perform a recurrent neural network cell activation is executed. The executing includes performing a plurality of operations of the recurrent neural network cell activation to provide a result of the recurrent neural network cell activation. The plurality of operations is performed in a single invocation of the instruction. The recurrent neural network cell activation is, for instance, a long short-term memory cell activation or a gated recurrent unit cell activation.

An instruction to perform a recurrent neural network cell activation is executed. The executing includes performing a plurality of operations of the recurrent neural network cell activation to provide a result of the recurrent neural network cell activation. The plurality of operations is performed in a single invocation of the instruction. The recurrent neural network cell activation is, for instance, a long short-term memory cell activation or a gated recurrent unit cell activation.

Various embodiments provide for computational systems including multiple circuit packages, each circuit package comprising an electronic integrated circuit having multiple processing elements and intra-chip bidirectional photonic channels connecting the processing elements into an electro-photonic network, with inter-chip bidirectional photonic channels connecting the processing elements across the electro-photonic networks of the multiple circuit packages into a larger electro-photonic network.

According to one embodiment, in a semiconductor integrated circuit, the plurality of storage devices are arranged in a form of a plurality of rows. Each of the storage devices are configured to store a bit position value of a weight of multiple bits. The plurality of multiplication circuits are arranged in a form of a plurality of rows and are configured to multiply a plurality of input voltages by the weight of multiple bits to generate a plurality of multiplication results. The one or more capacitive devices are configured to accumulate charges corresponding to the plurality of multiplication results. The adder circuit are configured to generate an output voltage corresponding to the total value of the charges accumulated in the one or more capacitive devices. The plurality of input voltages have different amplitudes. Each of the input voltages is associated with a corresponding bit position of the weight.

According to one embodiment, in a semiconductor integrated circuit, the plurality of storage devices are arranged in a form of a plurality of rows. Each of the storage devices are configured to store a bit position value of a weight of multiple bits. The plurality of multiplication circuits are arranged in a form of a plurality of rows and are configured to multiply a plurality of input voltages by the weight of multiple bits to generate a plurality of multiplication results. The one or more capacitive devices are configured to accumulate charges corresponding to the plurality of multiplication results. The adder circuit are configured to generate an output voltage corresponding to the total value of the charges accumulated in the one or more capacitive devices. The plurality of input voltages have different amplitudes. Each of the input voltages is associated with a corresponding bit position of the weight.

The present disclosure discloses a processing element and a neural processing device including the processing element. The processing element includes a weight register configured to store a weight, an input activation register configured to store input activation, a flexible multiplier configured to generate result data by performing a multiplication operation of the weight and the input activation by using a first multiplier of a first precision or using both the first multiplier and a second multiplier of the first precision in response to a calculation mode signal and a saturating adder configured to generate a partial sum by using the result data.

The present disclosure discloses a processing element and a neural processing device including the processing element. The processing element includes a weight register configured to store a weight, an input activation register configured to store input activation, a flexible multiplier configured to generate result data by performing a multiplication operation of the weight and the input activation by using a first multiplier of a first precision or using both the first multiplier and a second multiplier of the first precision in response to a calculation mode signal and a saturating adder configured to generate a partial sum by using the result data.

Aspects for Long Short-Term Memory (LSTM) blocks in a recurrent neural network (RNN) are described herein. As an example, the aspects may include one or more slave computation modules, an interconnection unit, and a master computation module collectively configured to calculate an activated input gate value, an activated forget gate value, a current cell status of the current computation period, an activated output gate value, and a forward pass result.

A semiconductor device executes the processing of a neural network. The memory MEM1 holds a plurality of pixel values and j compressed weighting factors. The decompressor DCMP restores the j compressed weighting factors to the uncompressed k (k≥j) weighting factors. The DMA controller DMAC1 reads the j compressed weighting factors from the memory MEM1 and transfers them to the decompressor DCMP. The n (n>k) accumulators in the accumulator unit ACCU multiply a plurality of pixel values and k uncompressed weighting factor to accumulate and add the multiplication results to the time series. A switch circuit SW1 provided between the decompressor DCMP and the accumulator unit ACCU transfers the k uncompressed weighting factors restored by the decompressor DCMP to n accumulators based on the correspondence represented by the identifier.

A semiconductor device executes the processing of a neural network. The memory MEM1 holds a plurality of pixel values and j compressed weighting factors. The decompressor DCMP restores the j compressed weighting factors to the uncompressed k (k≥j) weighting factors. The DMA controller DMAC1 reads the j compressed weighting factors from the memory MEM1 and transfers them to the decompressor DCMP. The n (n>k) accumulators in the accumulator unit ACCU multiply a plurality of pixel values and k uncompressed weighting factor to accumulate and add the multiplication results to the time series. A switch circuit SW1 provided between the decompressor DCMP and the accumulator unit ACCU transfers the k uncompressed weighting factors restored by the decompressor DCMP to n accumulators based on the correspondence represented by the identifier.