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