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公开(公告)号:EP4336409A1
公开(公告)日:2024-03-13
申请号:EP22195142.9
申请日:2022-09-12
IPC分类号: G06N3/0464 , G06N3/063
摘要: A convolutional neural network (101) includes convolution circuitry (106). The convolution circuitry (106) performs convolution operations on input tensor values. The convolutional neural network (101) includes requantization circuitry (112) that requantizes convolution values output from the convolution circuitry (106).
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公开(公告)号:EP4303771A1
公开(公告)日:2024-01-10
申请号:EP23177863.0
申请日:2023-06-07
IPC分类号: G06N3/063 , G06N3/0464 , G06N3/0985
摘要: A convolutional accelerator (112) includes a feature line buffer, a kernel buffer, a multiply-accumulate cluster, and iteration control circuitry (114). The convolutional accelerator (112), in operation, convolves a kernel with a streaming feature data tensor. The convolving includes decomposing the kernel into a plurality of sub-kernels and iteratively convolving the sub-kernels with respective sub-tensors of the streamed feature data tensor. The iteration control circuitry (114), in operation, defines respective windows of the streamed feature data tensors, the windows corresponding to the sub-tensors.
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公开(公告)号:EP4296900A1
公开(公告)日:2023-12-27
申请号:EP23178341.6
申请日:2023-06-09
发明人: ROSSI, Michele , BOESCH, Thomas , DESOLI, Giuseppe
IPC分类号: G06N3/0464 , G06N3/063 , G06F17/15
摘要: A convolutional accelerator includes a feature line buffer, a kernel buffer, a multiply-accumulate cluster, and mode control circuitry. In a first mode of operation, the mode control circuitry stores feature data in a feature line buffer and stores kernel data in a kernel buffer. The data stored in the buffers is transferred to the MAC cluster of the convolutional accelerator for processing. In a second mode of operation the mode control circuitry stores feature data in the kernel buffer and stores kernel data in the feature line buffer. The data stored in the buffers is transferred to the MAC cluster of the convolutional accelerator for processing. The second mode of operation may be employed to efficiently process 1xN kernels, where N is an integer greater than or equal to one.
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公开(公告)号:EP3531347A1
公开(公告)日:2019-08-28
申请号:EP19159074.4
申请日:2019-02-25
摘要: Embodiments of a device include on-board memory, an applications processor, a digital signal processor cluster, a configurable accelerator framework, and at least one communication bus architecture. The communication bus communicatively couples the applications processor, the digital signal processor cluster, and the configurable accelerator framework to the on-board memory. The configurable accelerator framework includes a reconfigurable stream switch and a data volume sculpting unit, which has an input and an output coupled to the reconfigurable stream switch. The data volume sculpting unit has a counter, a comparator, and a controller. The data volume sculpting unit is arranged to receive (1004) a stream of feature map data that forms a three-dimensional feature map. The three-dimensional feature map is formed as a plurality of two-dimensional data planes.
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15.
公开(公告)号:EP4428759A2
公开(公告)日:2024-09-11
申请号:EP24155570.5
申请日:2024-02-02
发明人: ROSSI, Michele , DESOLI, Giuseppe , BOESCH, Thomas
IPC分类号: G06N3/065 , G06N3/0464 , G06F13/40
CPC分类号: G06N3/065 , G06N3/0464 , G06F13/4022
摘要: A hardware accelerator (110) includes processing elements (172) of a neural network, each processing element having a memory (104); a stream switch (155); stream engines (150) coupled to functional circuits (102, 160, 165, 180) via the stream switch (155), wherein the stream engines (150), in operation, generate data streaming requests to stream data to and from functional circuits of the plurality of functional circuits (102, 160, 165, 180); a first system bus interface (158) coupled to the stream engines (150); a second system bus interface (184) coupled to the processing elements (172); and mode control circuitry (176), which, in operation, sets respective modes of operation for the plurality of processing elements (172). The modes of operation include: a compute mode of operation in which the processing element (172) performs computing operations using the memory (104) associated with the processing element; and a memory mode of operation in which the memory (104) associated with the processing element (172) performs memory operations, bypassing the stream switch (155), via the second system bus interface (184).
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16.
公开(公告)号:EP4418131A1
公开(公告)日:2024-08-21
申请号:EP24155592.9
申请日:2024-02-02
发明人: ROSSI, Michele , DESOLI, Giuseppe , BOESCH, Thomas
IPC分类号: G06F13/40 , G06N3/0464 , G06N3/063
CPC分类号: G06F13/4022 , G06N3/063 , G06N3/0464
摘要: A hardware accelerator (110) includes a plurality of functional circuits, a stream switch (155), and a plurality of stream engines (150). The stream engines (150) are coupled to the functional circuits via the stream switch (155), and in operation, generate data streaming requests to stream data to and from the functional circuits. The functional circuits include at least one convolutional cluster (170), which includes a plurality of processing elements (172) coupled together via a reconfigurable crossbar switch (174). The reconfigurable crossbar switch (174) is coupled to the stream switch (155), and in operation, streams data to, from, and between processing elements (172) of the processing cluster (170).
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17.
公开(公告)号:EP4428759A3
公开(公告)日:2024-10-09
申请号:EP24155570.5
申请日:2024-02-02
发明人: ROSSI, Michele , DESOLI, Giuseppe , BOESCH, Thomas
IPC分类号: G06N3/065 , G06N3/0464 , G06F13/40
CPC分类号: G06N3/065 , G06N3/0464 , G06F13/4022
摘要: A hardware accelerator (110) includes processing elements (172) of a neural network, each processing element having a memory (104); a stream switch (155); stream engines (150) coupled to functional circuits (102, 160, 165, 180) via the stream switch (155), wherein the stream engines (150), in operation, generate data streaming requests to stream data to and from functional circuits of the plurality of functional circuits (102, 160, 165, 180); a first system bus interface (158) coupled to the stream engines (150); a second system bus interface (184) coupled to the processing elements (172); and mode control circuitry (176), which, in operation, sets respective modes of operation for the plurality of processing elements (172). The modes of operation include: a compute mode of operation in which the processing element (172) performs computing operations using the memory (104) associated with the processing element; and a memory mode of operation in which the memory (104) associated with the processing element (172) performs memory operations, bypassing the stream switch (155), via the second system bus interface (184).
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18.
公开(公告)号:EP4455895A1
公开(公告)日:2024-10-30
申请号:EP24162857.7
申请日:2024-03-12
IPC分类号: G06F13/00 , H04L49/00 , H04L49/506 , G06N3/063 , G06N3/0464
摘要: A stream switch (130) includes a data router (132), configuration registers (138), and arbitration logic (140). The data router (132) has a plurality of input ports (134), each having a plurality of associated virtual input channels, and a plurality of output ports (136), each having a plurality of associated virtual output channels. The data router (132) transmits data streams from input ports (134) to one or more output ports of the plurality of output ports (136). The configuration registers (138) store configuration data associated with the virtual output channels of the respective output ports of the plurality of output ports (136). The stored configuration data identifies a source input port and virtual input channel ID associated with the virtual output channel of the output port. The arbitration logic (140) allocates bandwidth of the data router (132) based on request signals associated with virtual input channels of the input ports (134) and the configuration data associated with the virtual output channels.
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19.
公开(公告)号:EP4439387A1
公开(公告)日:2024-10-02
申请号:EP24162228.1
申请日:2024-03-08
IPC分类号: G06N3/0464 , G06N3/063
CPC分类号: G06N3/063 , G06N3/0464
摘要: A neural network (102) in a processing device (100) is able to reconfigure hardware accelerators (108) on-the-fly without stopping downstream hardware accelerators. The neural network (102) inserts a reconfiguration tag (140) into the stream of feature data. If the reconfiguration tag (140) matches an identification data (138) of a hardware accelerator (108), a reconfiguration process is initiated. Upstream hardware accelerators are paused while downstream hardware accelerators continue to operate. An epoch controller (112) in the processing device (100) reconfigures the hardware accelerator (108) via a bus (114). Normal operation of the neural network (102) then resumes.
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