cnn_chip

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A tiny Verilog CNN accelerator for the Tang Nano 20K. Features INT8 quantized weights, a streaming MAC array, and full on-chip MNIST inference.

cnn_chip 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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---

# Tiny CNN Chip running MNIST in Verilog

End-to-end MNIST digit-classifier on a **Tang Nano 20K** (Gowin GW2AR-18) FPGA. A tiny CNN — one 3×3 conv channel, ReLU, 2×2 max-pool, and a 169→10 fully connected layer — runs entirely on-chip in fixed-point INT8 arithmetic. Trained in PyTorch, quantized, and loaded into on-chip BSRAM/pROM blocks.

## Demo

[Watch Demo Video](https://github.com/123-code/cnn_chip/raw/main/media/demo.mov)

The terminal shows the input image as ASCII art, then the FPGA's LEDs light up to spell the predicted digit in binary (active-low: lit LED = 1 bit). See `demo_flash.sh` to reproduce.

Two flavors of the design live in this repo, sharing the same compute datapath:

* **LED version** — image baked into the bitstream; result shown on six on-board LEDs. Headless "power on → see the answer" demo.
* **UART version** — image streamed in over UART at runtime; result sent back as one byte. Used for development and batch verification from a host PC.

Two versions of the convolution itself ship in this repo, selectable via a Verilog parameter:

* **v1 (main)** — 1-multiplier serial scan. ~27 cycles per output pixel. The original design that shipped to silicon.
* **v2 (v2-parallel-conv)** — 9-multiplier streaming MAC array with 2×28 line buffers and a 3×3 register window. **1 output pixel per cycle.** Bit-identical to v1 at the FC output; validated in iverilog (8.25× fewer compute cycles on the same test image) and flashed to the live board.

---

## Accuracy

| Model | Test set | Accuracy |
| --- | --- | --- |
| PyTorch float32 (CPU) | MNIST test (10000) | 91.80% |
| PyTorch float32 (CPU) | 50 sampled images | 96.0% |
| **FPGA chip** (INT8 quantized, fixed-point) | 50 sampled images | **94.0%** |

The chip accuracy was produced by a bit-accurate simulator (`model/hw_sim.py`) that performs the **exact same** fixed-point operations the FPGA does and reads the **exact same** `.mi` byte streams the FPGA loads into its ROMs at config time. We separately validated the simulator against the real hardware: the live FPGA classifies each individual image to the same digit the simulator predicts. See `model/batch_meta.json` for the full per-image breakdown.

The quantization gap (chip 94.0% vs CPU 96.0% on the same 50 images) is the cost of compressing the model to INT8 weights + an `acc >> 8` activation scale that fits in a `acc[15:8]` output byte. Larger models with more channels could close this gap.

---

## Architecture Diagrams

### Top level layout

```text
Host PC (Python Script)
                        ▲   │
             tx_out     │   │ rx_in
            (serial)    │   │ (serial)
                        │   ▼
 ┌─────────────────────────────────────────────────────────────┐
 │ Tang Nano 20K Boundary                                      │
 │                                                             │
 │  ┌────────────┐               ┌──────────────────────────┐  │
 │  │            │rx_byte[7:0]   │                          │  │
 │  │ UART RX/TX ├──────────────▶│     Input Image SRAM     │  │
 │  │            │   write_en    │ (Single-Port, Hard IP)   │  │
 │  └─────▲──────┘               └─────────────▲────────────┘  │
 │        │                                    │               │
 │        │                               read_addr[9:0]       │
 │        │pred_digit[3:0]                     │               │
 │        │tx_start      ┌───────────────┐     │               │
 │        │              │               │─────┘               │
 │        │              │ Main Control  │                     │
 │        │              │      FSM      │rom_addr[15:0],read_en
 │        │              │               │─────┐               │
 │        │              └───────┬───────┘     │               │
 │        │   start_layer (held) │             ▼               │
 │        │   layer_type         │  ┌───────────────────────┐  │
 │        │                      │  │   Weights pROM (IP)   │  │
 │        │      layer_done      │  ├───────────────────────┤  │
 │        │                      ▼  │    Bias pROM (IP)     │  │
 │  ┌─────┴─────────────────────────┴───────────────────────┐  │
 │  │                                                       │  │
 │  │       Compute Pipeline (Conv -> Pool -> FC)           │◀─┼─ weight_val[7:0]
 │  │                                                       │◀─┼─ bias_val[31:0]
 │  └────────────────────────────▲──────────────────────────┘  │
 │                               │                             │
 │                         pixel_val[7:0]                      │
 └─────────────────────────────────────────────────────────────┘

```

### Conv pipeline — v1 (serial scan, 1 multiplier)

```text
[ Input Image SRAM ]                [ Weight pROM ]
          │                                 │
    pixel_val[7:0]                    weight_val[7:0]
          │                                 │
          ▼                                 ▼
       ┌───────────────────────────────────────┐
       │             MULTIPLIER                │ ◀── (Only ONE multiplier)
       └──────────────────┬────────────────────┘
                          │
                   (16-bit signed)
                          │
                          ▼
       ┌───────────────────────────────────────┐
 ┌───▶ │               ADDER                   │ ◀── (Replaces an 8-adder tree)
 │     └──────────────────┬────────────────────┘
 │                        │
 │                        ▼
 │     ┌───────────────────────────────────────┐
 └─────┤         ACCUMULATOR REGISTER          │
       └──────────────────┬────────────────────┘
                          │
                          │ (Outputs only after 9 tap cycles)
                          ▼
                  [ ReLU & >> 8 ]
                          │
                          ▼
                    conv_out[7:0]

```

*> ~27 cycles / output pixel · 9 MACs serialized · 0 line-buffer storage*

### Conv pipeline — v2 (parallel 9-MAC, streaming window)

```text
                  [ Weight pROM ]
                          │
                          ▼
              ┌───────────────────────┐
              │ Preload FSM (~10 cyc) │ — fetch weights[0..8]
              └───────────┬───────────┘
                          ▼
                 w[0..8] (registered)
                          │
[ Input Image SRAM ]      │
          │               │
    pixel_in[7:0]         │
          │               │
          ▼               │
  ┌────────────────┐      │
  │ Line buffer 0  │ (28 deep)
  └────────┬───────┘      │
           ▼              │
  ┌────────────────┐      │
  │ Line buffer 1  │ (28 deep)
  └────────┬───────┘      │
           ▼              │
  ┌────────────────────┐  │
  │ 3×3 register window │ ─┐
  └────────┬────────────┘  │
           ▼               ▼
       ┌──────────────────────────┐
       │   9 PARALLEL MULTIPLIERS │
       └────────────┬─────────────┘
                    │ 9 × 16-bit products
                    ▼
       ┌──────────────────────────┐
       │   8-input ADDER TREE     │ (combinational)
       └────────────┬─────────────┘
                    │
                    ▼
            [ ReLU & >>> 8 ]
                    │
                    ▼
              conv_out[7:0]

```

*> 1 cycle / output pixel · 9 parallel MACs · 2 × 28-byte line buffers · Total conv: ~786 cycles for 26×26 output (vs ~17,000 in v1)*

### FC pipeline (per-digit dot product + bias + argmax)

```text
[ Max Pool Pipeline ]               [ Weight pROM ]
          │                                 │
   pool_pixel_val[7:0]                weight_val[7:0]
          │                                 │
          ▼                                 ▼
       ┌───────────────────────────────────────┐
       │             MULTIPLIER                │
       └──────────────────┬────────────────────┘
                          │
                          ▼
       ┌───────────────────────────────────────┐
 ┌───▶ │               ADDER                   │
 │     └──────────────────┬────────────────────┘
 │                        │
 └─────[   FC Accumulator Register (32-bit)    ]
                          │
                          │ (After all 169 FC weights summed for one digit)
                          ▼
       ┌───────────────────────────────────────┐         [ Bias pROM ]
       │            FINAL BIAS ADDER           │ ◀───────  bias_val[31:0]
       └──────────────────┬────────────────────┘
                          │
                          ▼
                 (Total Score + Bias)
                          │
                          ▼
       ┌───────────────────────────────────────┐
       │           ARGMAX COMPARATOR           │
       │    (if Score > highest_score)         │ ────▶ [ predicted_digit ]
       └───────────────────────────────────────┘

```

---

## Memory layout

| ROM/RAM | Depth | Width | Contents |
| --- | --- | --- | --- |
| `mem_image_ram` | 784 | 8 | 28×28 uint8 image |
| `weights_rom` | 1699 | 8 | Addr 0–8: conv 3×3 kernel<br>

<br>Addr 1–10: FC biases (int32, two's complement) |

LED version uses Gowin **SP/pROM** hard IPs with `.mi` init files. UART version uses inferred **reg-array** memories with `$readmemh`.

---

## Quantization & hardware/software contract

The Python model is `float32`. The FPGA is fixed-point. To make them match, the training script enforces several constraints that aren't optional:

| Constraint | Why |
| --- | --- |
| `nn.Conv2d(..., bias=False)` | Hardware conv MAC chain has no bias adder; a learned conv bias would be silently dropped. |
| Pixel input scale: float `[0,1]` → uint8 `[0,255]` | `transforms.ToTensor()` gives float; FPGA reads bytes. Implicit ×255 scale. |
| Conv weight quantization: `round(w * 127/max(w))` | Adjusts float values into 8-bit signed integer capacity. |
| Conv output: `(acc >> 8)` clamped to `[0,255]` after ReLU | Drops 8 bits ≈ ÷256 — accommodates accumulated 9-MAC range. |
| FC bias quantization: `round(b * fc_scale * conv_scale * 255/256)` | FC bias is added to a hardware accumulator that's already at scale `conv_scale × fc_scale`. Scaling biases by `fc_scale` alone (the obvious choice) makes them ~100× too small. |

---

## v1 → v2: from serial scan to parallel streaming

The chip originally shipped with `conv_serial.v` — one multiplier, ~27 cycles per output pixel. A parallel `conv_sliding_win.v` + `mac_array_3x3.v` design existed in the repo but was set aside; it turned out to contain several real bugs (not polish issues), which is why v1 went with the conservative serial path.

The `v2-parallel-conv` branch goes back and finishes that work properly:

| Bug in the legacy prototype | Fix in v2 |
| --- | --- |
| `mac_array_3x3` added the FC bias into every conv output | Removed — conv has no bias (`nn.Conv2d(bias=False)`) |
| All 9 weight ports wired to the same `weight_in` ("simplified for now") | New preload FSM fetches the 9 conv kernel weights into a register file |
| `done` fired on the last input pixel — missed the trailing MAC outputs | Explicit 3-cycle drain after last `mac_valid_in` |
| No 2-cycle ROM/SRAM latency model — window contents off by one row | All addressing offset for the actual `posedge`→`posedge` chain |

The new `compute_pipeline.v` exposes a `parameter PARALLEL_CONV` (default 1) that selects between the two conv implementations via `generate`:

```verilog
compute_pipeline #(.PARALLEL_CONV(1)) u_compute (...);  // v2 (default)
compute_pipeline #(.PARALLEL_CONV(0)) u_compute (...);  // v1

```

| Metric (sim, single image) | v1 (serial) | v2 (parallel) |
| --- | --- | --- |
| **Conv multipliers** | 1 | 9 |
| **Line-buffer storage** | 0 B | ~56 B |
| **Conv throughput** | ~27 cyc/px | 1 px/cyc |
| **Compute cycles to `layer_done**` | 20,632 | 2,501 (8.25×) |
| **Predicted digit** (same image) | 5 | 5 (bit-identical) |

For one-shot MNIST inference both finish faster than a human can blink. v2 isn't faster *to a user*; what it demonstrates is that the parallel datapath actually works on real silicon, end-to-end, with bit-identical math to the serial reference.

---

## Power-on reset (both versions)

The Tang Nano 20K's reset button reads stuck-low on the board we tested. To avoid holding the design in permanent reset, the top module synthesizes its own POR:

```verilog
reg [3:0] por_cnt = 4'd0;
reg       safe_rst_n_r = 1'b0;

always @(posedge clk) begin
    if (por_cnt != 4'd15) begin
        por_cnt      <= por_cnt + 4'd1;
        safe_rst_n_r <= 1'b0;
    end else begin
        safe_rst_n_r <= 1'b1;
    end
end
wire safe_rst_n = safe_rst_n_r;

```

This matters more than it looks. Without a real reset pulse Gowin's synthesizer leaves some FFs at undefined power-on values — most damagingly `fc_layer.highest_score`, which needs to start at `-2 × 10⁹` for the argmax comparison to work. Standalone `initial begin … end` blocks turned out to be unreliable on this toolchain; inline-declaration initializers (`reg [3:0] x = 4'd0;`) and a real reset pulse work.

---

## Repository layout

```text
.
├── top_mnist_accel.v          # UART-version top (this dir is the UART build)
├── control_unit.v             # UART FSM: IDLE → LOAD_IMG → COMPUTE → TX_RESULT
├── compute_pipeline/
│   ├── compute_pipeline.v     # conv + pool + fc orchestration; PARALLEL_CONV selects v1/v2
│   ├── conv_serial.v          # v1: serial 3×3 convolution (1 mul)
│   ├── conv_sliding_win.v     # v2: streaming 3×3 conv (line buffers + 3×3 window)
│   ├── mac_array_3x3.v        # v2: 9-MAC adder tree + ReLU + quantize
│   ├── max_pool_2x2.v         # streaming 2×2 max-pool
│   └── fc_layer.v             # 169→10 FC, argmax with bias
├── mem_image_ram.v            # 784×8 inferred RAM (UART writes, compute reads)
├── mem_weights_rom.v          # 1699×8 weights + 11×32 biases, $readmemh
├── uart_rx.v · uart_tx.v      # 115200-baud serial peripherals
├── pins.cst                   # Tang Nano 20K pin mapping
├── tb_top.v                   # iverilog testbench (sends 784 bytes via UART)
├── weights.hex / weights.mi   # quantized weight ROM (.hex for $readmemh, .mi for Gowin IP)
├── bias.hex / model/bias.mi   # quantized FC bias ROM
├── image.mi                   # currently-loaded test image (28×28 bytes)
├── model/
│   ├── train.py               # PyTorch model + quantization + ROM export
│   └── hw_sim.py              # Python hardware-accurate inference simulator
└── software/
    └── send_image.py          # host-side serial driver for the UART version

```

LED-version sources live separately under the Gowin project tree. They are the same modules with two differences: the top uses LEDs/baked image instead of UART/streamed image, and the memories are Gowin SP/pROM hard IPs instead of inferred RAM.

---

## Building & running

### Train and export weights

```bash
python -m venv venv
source venv/bin/activate
pip install torch torchvision numpy
python model/train.py        # writes model/weights.hex and model/bias.mi

```

### Simulate with Icarus Verilog

**v1 (serial conv)** on `main`:

```bash
iverilog -g2012 -o sim_v1.vvp \
  tb_top.v top_mnist_accel.v control_unit.v \
  compute_pipeline/compute_pipeline.v \
  compute_pipeline/conv_serial.v \
  compute_pipeline/max_pool_2x2.v \
  compute_pipeline/fc_layer.v \
  mem_image_ram.v mem_weights_rom.v \
  uart_rx.v uart_tx.v
vvp sim_v1.vvp

```

**v2 (parallel conv)** on `v2-parallel-conv`:

```bash
iverilog -g2012 -o sim_v2.vvp \
  tb_top.v top_mnist_accel.v control_unit.v \
  compute_pipeline/compute_pipeline.v \
  compute_pipeline/conv_serial.v \
  compute_pipeline/conv_sliding_win.v \
  compute_pipeline/mac_array_3x3.v \
  compute_pipeline/max_pool_2x2.v \
  compute_pipeline/fc_layer.v \
  mem_image_ram.v mem_weights_rom.v \
  uart_rx.v uart_tx.v
vvp sim_v2.vvp

```

`tb_top.v` prints `RESULT predicted_digit=N compute_cycles=N` on `layer_done`. Both builds must predict the same digit on the same image — that's the equivalence check.

`model/hw_sim.py` runs the same fixed-point math in Python against the same `.mi` byte streams — useful for verifying **what the hardware should predict** before reflashing.

### Synthesize for Tang Nano 20K

1. Open the Gowin project — or create a new one targeting `GW2AR-LV18QN88C8/I7` with the Verilog sources from this tree and `pins.cst`.
2. Regenerate the SP/pROM IPs pointing at `image.mi`, `weights.mi`, `bias.mi`.
3. Synthesize → Place & Route → Program Device.

### Run inference (UART version)

```bash
# Opens /dev/tty.usbserial-* at 115200, sends 784 bytes, reads 1 byte back.
python software/send_image.py path/to/digit.png

```

### Run inference (LED version)

Power on. Wait 1 s. Read LEDs:

| LED | Meaning |
| --- | --- |
| **5** | Heartbeat (toggles ≈3 Hz; confirms FPGA clocking) |
| **4** | Before math: `~fsm_started` · After math: `~predicted_digit[3]` |
| **3** | `~math_done` (on = math finished) |
| **2:0** | `~predicted_digit[2:0]` |

LEDs are active-low — output `0` lights the LED. Example: digit 7 = `0111` → LEDs 0/1/2 ON, 3 ON, 4 OFF, 5 blinking.

---

## Bringup notes

Things that bit us during bringup, preserved here so they don't bite again:

* **Dead reset paths don't bake INIT values on Gowin.** Hardwiring `safe_rst_n = 1'b1` makes every `if (!rst_n) … else …` block dead code, and Gowin won't extract the reset values as FF init attributes. Use a POR counter.
* **FC weight fetch had a 1-cycle off-by-one.** ROM is bypass-mode (1-cycle latency); the FSM was setting `rom_addr_out <= 9` and burning a cycle in `S_WAIT_ROM`, so `buffer[0]` got multiplied by `weights[10]` instead of `weights[9]`. Fix: start at `8`.
* **FC argmax needs to be seeded.** Without `if (digit_counter == 0 || score > highest_score)`, an image where all 10 dot products are negative leaves `winning_digit` stuck at its init value.
* **The hardware has no conv bias adder.** Training with `bias=True` on `nn.Conv2d` silently throws away a learned parameter and corrupts ReLU thresholds.
* **FC bias must be scaled** by `conv_scale × fc_scale`, not just `fc_scale`, because it adds into an already-scaled accumulator.
* **`predicted_digit` is 4 bits but the board has 6 LEDs.** Wire the high bit to LED4 (mux'd with `fsm_started` pre-math) or you can't distinguish 0/8, 1/9, 2/10.

### v2 bringup notes

* **Single-port ROM means the 9 conv weights can't be fetched in parallel.** The parallel MAC array needs all 9 weights *simultaneously*, but `mem_weights_rom` only delivers one byte per cycle. Solution: a 10-cycle preload FSM that walks ROM addresses 0..8 once at the start of inference and latches into a `reg signed [7:0] w [0:8]` register file. Streaming then runs from registers, with the ROM idle (free for FC to use later).
* **The ROM and SRAM both have 2-cycle issue→read latency.** Registered output on the memory module + the always-block delay = `weight_in(T) = w_rom[rom_addr at end of T-2]`. Forgetting this gives every weight an off-by-one and zero correct outputs. The preload schedule has to interleave issues and latches so the first stream cycle sees `ram[0]` exactly.
* **Pipeline drain matters.** The MAC array has 2 register stages (products → adder tree → output). After the last `mac_valid_in` pulse, `done` cannot fire for at least 2 more cycles or the last conv output gets dropped before max-pool can consume it. The current implementation waits 3 cycles to be safe.
* **Window validity is geometric, not temporal.** The 3×3 window's bottom-right corner walks the input in raster order; a valid output requires the corner to be at `(row >= 2, col >= 2)`. When `col` wraps from 27 → 0 at a row boundary, the leftmost two outputs of the new row are invalid — `mac_valid_in` must drop. Easy to get wrong by 26 outputs.
* **Generate-blocks let v1 and v2 coexist.** `compute_pipeline.v` uses a `parameter PARALLEL_CONV` + `generate / if` to instantiate either `conv_serial` or `conv_sliding_win`. The unused module is optimized out by synthesis — no extra fabric cost — but iverilog still typechecks both branches, so you find dead-code bugs early.
* **Iverilog testbench at 50 MHz silently breaks UART injection.** `uart_rx` defaults to `parameter CLK_FREQ = 27_000_000`. The sim testbench clocks the chip at 50 MHz without overriding the parameter, so the receiver samples every bit twice and the FSM transitions to `COMPUTE` mid-injection with a partially-loaded RAM. Both v1 and v2 sim with this bug, which is **why both predict the same "wrong" digit** — the v1↔v2 equivalence check works regardless. On real hardware with a 27 MHz clock or the LED-version baked image, the chip predicts correctly.

---

## Physical constraints

The `pins.cst` file contains the Tang Nano 20K pin mapping (clock at pin 4, LEDs at pins 15–20, UART/reset pins as configured).

## License

MIT.

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