mirror of
https://github.com/laanwj/k210-sdk-stuff.git
synced 2024-11-25 02:46:18 +04:00
276 lines
11 KiB
Markdown
276 lines
11 KiB
Markdown
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KPU
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===
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Some notes about the K210 KPU, which is definitely the weirdest, possibly
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most interesting peripheral on this SoC. Documentation doesn't seem to be
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available, so the information here has been reconstructed from various vendor
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source code.
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This kind of custom hardware is pretty much impossible to understand without
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knowledge of the domain, in this case Convolutional Neural Networks on images.
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My understanding of this is rudimentary (my last brush with it was in uni) so
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I may be missing some obvious clues here and there.
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From the datasheet
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==================
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The Kendryte datasheet has the following information on the KPU:
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> KPU is a general-purpose neural network processor with built-in convolution,
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> batch normalization, activation, and pooling operations. It can detect faces or
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> objects in real time. The specific characteristics are as follows:
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>
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> - Supports the fixed-point model that the mainstream training framework trains
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> according to specific restriction rules
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> - There is no direct limit on the number of network layers, and each layer of
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> convolutional neural network parameters can be configured separately, including
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> the number of input and output channels, and the input and output line width
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> and column height
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> - Support for 1x1 and 3x3 convolution kernels
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1×1 and 3×3 is not a very wide range of supported convolutions, but maybe the most
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common ones in this specific application area…
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> - Support for any form of activation function
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This is definitely true, Normalization functions seem to be represented as an array of 16
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segments (`kpu_activate_table_t`).
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> - The maximum supported neural network parameter size for real-time work is 5MiB
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> to 5.9MiB
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> - The maximum supported network parameter size when working in non-real time is
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> (flash size - software size)
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The flash size specs are somewhat of a red herring as they relate to software
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instead of hardare: the KPU does not have logic for loading parameters from
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flash.
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Some other source mentions:
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> 64 KPU which are 576bit width, supports convolution kernel. Offers
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> 0.25TOPS@0.3W,400MHz, and when you overclock to 800MHz, it offers 0.5TOPS,
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> meaning you can do object recognition 60fps@VGA.
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Clock speed
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===========
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The KPU is clocked from PLL1, with a divisor between 1 and 16.
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The usual clock speed in the Sipeed examples is 300, sometimes 400 MHz.
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According to some mentions in the data sheet it's possible to clock it to 800 MHz.
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Overall execution flow
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======================
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The overall execution flow is that the KPU runs a neural network layer by
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layer. This happens in a sequential fashion. Each layer can be considered a
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separate set of instructions for the KPU.
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A layer can receive its input in the "AI" memory area (2MB of the memory is reserved for this,
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from 0x40600000 to 0x407fffff) as well as write its output there. The input and
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output can consist of multiple channels (R/G/B for example).
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It is possible to set an interrupt to notify the host CPU when a specific layer has
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finished executing.
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Looking at `lib/drivers/kpu.c` in the SDK, function `ai_step`, many types of CNN layers are
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implemented in software instead of executed by the KPU. I suppose they accelerated the
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most common multiplication-intensive layers in hardware, which is `KL_K210_CONV`.
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Peripehral layout
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=================
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The register layout of the peripheral is as folllows. Source: `lib/drivers/include/kpu.h`.
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All registers are 64-bit.
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| Ofs | Name | Description |
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| ----- | ----------------- | ------------------------------------------------------------- |
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| 0x00 | `layer_argument_fifo` | Layer arguments (instructions) are submitted here |
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| 0x08 | `interrupt_status` | Status of pending interrupts |
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| 0x10 | `interrupt_raw` | |
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| 0x18 | `interrupt_mask` | Specifies which global interrupts are enabled |
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| 0x20 | `interrupt_clear` | Clear pending interrupts |
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| 0x28 | `fifo_threshold` | FIFO interrupt thresholds |
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| 0x30 | `fifo_data_out` | Data output FIFO read register |
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| 0x38 | `fifo_ctrl` | Flush FIFOs |
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| 0x40 | `eight_bit_mode` | Enable 8-bit instead of 16-bit precision |
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Layer format
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============
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KPU neural network layers are represented by a series of 12 64-bit values,
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submitted to the layer argument FIFO one by one. The overall structure of the bit fields is
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available in `lib/drivers/include/kpu.h`.
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It looks like the generation of models is supposed to be done offline by a tool called [nnscase](https://github.com/kendryte/nncase),
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which compiles TensorFlow models to a specific internal representation.
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The k210-specific code parts are [k210_ops.cpp](https://github.com/kendryte/nncase/tree/master/src/codegen/ops/k210/k210_ops.cpp)
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and [k210_sim_types.h](https://github.com/kendryte/nncase/blob/master/src/common/include/runtime/k210/k210_sim_types.h)
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and [k210_ops_body.h](https://github.com/kendryte/nncase/blob/master/src/common/include/runtime/k210/k210_ops_body.h)
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(serialization and deserialization).
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src/common/include/kernels/k210/k210_kernels.h (emulation)
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0 `interrupt_enabe`
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-------------------
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bit name
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------ ----------------------
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0 `int_en` Generate interuupt after layer computation finished
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1 `ram_flag` ?
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2 `full_add` Set in `kpu_conv2d_output_full_add`
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3 `depth_wise_layer` Is a "depth-wise" layer (1 if enabled)
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4..63 reserved
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"depth-wise" affects meny of the computations: it likely means that the layer
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computation mixes multiple channels so that they cannot be processed one by
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one.
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1 `image_addr`
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--------------
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bit name
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------ ----------------------
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0..14 `image_src_addr` Image source address
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15 reserved
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16..30 `image_dst_addr` Image destination address
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31..63 reserved
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`image_src_addr` and `image_dst_addr` are specified in 64-byte units relative to the base of "AI" memory.
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2 `image_channel_num`
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---------------------
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bit name
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------ ----------------------
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0..9 `i_ch_num` Number of input channels (minus one)
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10..31 reserved
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32..41 `o_ch_num` Number of output channels (minus one)
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42..47 reserved
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48..57 `o_ch_num_coef` Number of output channel coefficients (minus one)
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58..63 reserved
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3 `image_size`
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--------------
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bit name
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------ ----------------------
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0..9 `i_row_wid` Input row width (minus one)
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10..18 `i_col_high` Input column height (minus one)
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19..31 reserved
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32..41 `o_row_wid` Output row width (minus one)
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42..50 `o_col_high` Output column height (minus one)
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51..63 reserved
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4 `kernel_pool_type_cfg`
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------------------------
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bit name
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------ ----------------------
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0..2 `kernel_type` `filter_type_t` (see below)
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3 `pad_type` Always 1
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4..7 `pool_type` `pool_type_t` (see below)
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8 `first_stride` ?
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9 `bypass_conv` ?
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10 `load_para` Load parameters (1 if enabled)
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11..15 reserved
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16..23 `dma_burst_size` Always 15
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24..31 `pad_value` Padding value
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32..63 `bwsx_base_addr` Batch normalization array base address (8-aligned, `kpu_batchnorm_argument_t`)
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`kpu_filter_type`:
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value enum
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------ -----------
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0 1x1
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1 3x3
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`kpu_pool_type`:
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value enum description
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------ -------------- ------------------
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0 bypass bypass pooling (filter size 1×1, stride 1)
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1 max_2_s2 max pooling (filter size 2×2, stride 2)
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2 mean_2_s2 mean pooling (filter size 2×2, stride 2)
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3 max_4_s4 max pooling (filter size 4×4, stride 4)
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4 mean_4_s4 mean pooling (filter size 4×4, stride 4)
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5 left_top_2_s2 pick left top (filter size 2×2, stride 2)
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6 right_top_2_s2 pick right top (filter size 2×2, stride 2)
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7 left_top_4_s4 pick left top (filter size 4×4, stride 4)
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8 mean_2_s1 mean pooling (filter size 2×2, stride 1)
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9 max_2_s1 max pooling (filter size 2×2, stride 1)
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See `kpu_pool2d` in `src/common/include/kernels/k210/k210_kernels.h`,
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as well as `src/common/include/runtime/k210/k210_runtime_op_utility.h` in nncase.
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5 `kernel_load_cfg`
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-------------------
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bit name
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------ ----------------------
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0 `load_coor` Always 1
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1..6 `load_time` Parameter load frequency (0=once, 1=per channel?)
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7..14 reserved
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15..31 `para_size` Parameter (weights) size
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32..63 `para_start_addr` Parameter (weights) start address (128-aligned, one byte per weight)
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6 `kernel_offset`
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-----------------
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bit name
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------ ----------------------
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0..3 `coef_column_offset` ?
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4..15 `coef_row_offset` ?
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16..63 reserved
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7 `kernel_calc_type_cfg`
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------------------------
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bit name
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------ ----------------------
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0..14 `channel_switch_addr` In layout channel length
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15 reserved
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16..19 `row_switch_addr` In layout row length
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20..27 `coef_size` ?
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28..30 `coef_group` ?
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31 `load_act` Load activation function (1 is enabled)
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32..63 `active_addr` Activation function address (256-aligned `kpu_activate_table_t`)
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8 `write_back_cfg`
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------------------
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bit name
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------ ----------------------
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0..14 `wb_channel_switch_addr` Out layout channel length
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15 reserved
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16..19 `wb_row_switch_addr` Out layout row length
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20..22 `wb_group` Out layout number of groups
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23..63 reserved
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9 `conv_value`
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--------------
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bit name
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------ ----------------------
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0..3 `shr_w` Convolution value shift right w
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4..7 `shr_x` Convolution value shift right x
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8..31 `arg_w` Convolution value w multiplier
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32..55 `arg_x` Convolution value x multiplier
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56..63 reserved
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10 `conv_value2`
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----------------
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bit name
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------ ----------------------
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0..39 `arg_add` Convolution value addition/bias
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40..63 reserved
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11 `dma_parameter`
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------------------
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bit name
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------ ----------------------
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0 `send_data_out` Send data out to DMA (main memory)
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1..15 reserved
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16..31 `channel_byte_num` Number of bytes per out channel (minus one)
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32..63 `dma_total_byte` Number of bytes total out (minus one)
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