Yes Intel & AMD & Coral Edge TPU & like-minded instructions for parallel array processing:
Well defined Bundled 8Bit Parameterisation:
Firstly as stated in documents by myself before the RGB+BW 8,8,8,8 colour system developed by myself is a first rate utility to process 8Bit defined colours in HDR,
You can use 4,4,4,4 & any array of 8Bit precision or lower colour definition, For Planar Textures.
Secondly, Machine learning, defined in 8Bit is not beyond the capacities of Man's brain to define!
Most Humans & some types of animals think at a base level in 8Bit, Humans bundle 8Bit into higher precision, such as eyes.. & so forth..
Squid & Octopuses bundle _bit, Upto 96Bit Colours! So yes the system is well defined!
So you can think about 8Bit bundling as a very early thinking life-form evolutionary system for advancement..
You do need to define parameters affected by 8bit,.. With care,
With matrices of memory arrays, In higher definition, 8Bit weights may seem effective, 8bit maths may seem effective!
But we do need to optimise!
So there are Weight & Parameter machine learning models that are parametrized in 2Bit & upto 16Bit (in most cases),
We could use 64Bit & 32Bit, The CPU is a case point, where this matters.
So there are a lot of functions to consider, Work & thought are required, This is most important..
Remember Buddha & mentalists, Mathematicians, Physicists & Scientists & Psychologists & biologists, Optimise this path.
(c)Rupert Summerskill
*
Core ideas:
Main thesis: Practical, high‑performance ML inferencing and image/video processing can be built around low‑bit (4–8 bit) representations and SIMD/AVX/NPU arrays,..
With careful tiered precision, compression, and memory alignment to preserve accuracy while massively improving throughput and power efficiency.
Key themes: 8‑bit as a sweet spot for human‑like inference; quantization strategies (4→8→16→32 bit); packed‑bit SIMD math,..
Tiered caching and transparent precision casting; matrix/AVX/TPU mapping; wavelet/brotli compression for tensors,..
hardware choices (EdgeTPU/Coral, Movidius, Hailo, AVX/Intel/AMD).
Applied domains: image upscaling/edge detection, HDR/WCG color handling, medical imaging (ResNet‑style detection), low‑power edge inference, and database/statistics preprocessing for ML.
Architectural recommendations: use aligned memory blocks (8×8, 16×16), local DMA and 64–128‑byte cache-friendly transfers, prefetching, loop unrolling, and micro‑kernel dequantization for FP16/FP32 when needed.
Practical implementation checklist (engineer‑ready):
Model preparation
Train or fine‑tune in FP32/F16; export to ONNX/TFLite.
Apply post‑training quantization to INT8; evaluate AWQ/AWQ‑like methods for 4‑bit activation/weight cases.
Keep a small FP16 “remainder” path for critical layers (first/last, attention heads).
Tiered runtime
Load stage: read tensors in higher precision (F32/F16) for sorting/selection.
Cache stage: compress with Brotli‑G or wavelet autoencoder for large tensors; store compressed blocks in RAM.
Infer stage: decompress into INT8/INT4 packed buffers; run SIMD/TPU kernels.
Dequantize stage: when needed, run a fast dequant kernel to FP16 for layers that require float remainder.
Memory & packing
Use packed layouts: 32‑bit = 4×8b, 64‑bit = 8×8b, 128‑bit = 16×8b.
Align DMA transfers to cache line sizes (64B) and GPU bus widths (128/256/512 bits).
For add/mul chains, reserve a small extra bit per lane (carry/guard) to avoid overflow in packed arithmetic.
Hardware mapping
Edge/embedded: Coral EdgeTPU (INT8), Movidius (INT8), Hailo (TOPs) — use for low‑latency, low‑power inferencing.
Desktop/server: AVX2/AVX512 SIMD for packed INT8/INT16; use dp4a/dot‑product intrinsics where available.
Hybrid: Offload matrix multiplies to NPU/TPU and keep control/branching on CPU; use local DMA to avoid CPU/GPU thrash.
Algorithmic optimizations
Depthwise separable convs (DS‑CNN) and BNN/TNN for extreme compression.
Use wavelet autoencoders to compress repetitive patterns before quantization.
For edge detection/upscaling: combine small fixed‑point SiMD kernels (fast) with occasional float refinement passes.
RS
Summary of goals for document
We argue that 8‑bit parameterization is a principled design space—useful for color pipelines, texture formats, and ML inference,..
Rather than a mere optimization hack..
You want practical, system‑level ways to make 8‑bit (and nearby low‑bit) computation reliable: quantization strategies, parameter sensitivity, hardware mapping (SIMD/TPU/GPU), and perceptual/functional metrics that guide when to bundle or expand precision.
---
Recommended deliverables
| Option | Purpose | Key outputs |
|---|---:|---|
| A — Formalize 8‑bit sensitivity metrics | Quantify how model outputs change with bit reductions | Definitions; formulas for sensitivity; test harness; example results on a small model |
| B — Map perceptual error to quantization noise | Tie visual/ perceptual metrics to numeric quantization choices for textures/HDR | Dataset list; experiments (PSNR/SSIM/LPIPS); mapping curves; decision thresholds for 4:4:4 vs 4:2:2 |
| C — Reference 8‑bit inference pipeline | End‑to‑end blueprint for deploying 8‑bit inference on SIMD/TPU/GPU | Quantization scheme; accumulation rules; mixed‑precision policy; calibration steps; code sketch and test plan |
---
Concrete plan for Option C — Reference 8‑bit inference pipeline
1. Goals and constraints
- Target: deterministic inference with minimal accuracy loss vs FP32 baseline.
- Hardware: SIMD (x86/ARM), Coral Edge TPU, GPUs with 8‑bit matrix ops.
- Workloads: CNNs for image tasks, transformer blocks for small language/vision models, planar texture transforms.
2. Quantization primitives and notation
- Quantize a real tensor \(x\) to \(k\)-bit integer \(q\) using scale \(s\) and zero point \(z\):
\[
q = \mathrm{clip}\left(\left\lfloor \frac{x}{s} \right\rceil + z,\; q_\text{min},\; q_\text{max}\right)
\]
where \(q_\text{min}=0,\; q_\text{max}=2^k-1\) for unsigned, or symmetric signed range for signed formats.
- Dequantize:
\[
\hat{x} = s \cdot (q - z)
\]
3. Per‑tensor vs per‑channel
- Per‑channel scales for weights in convolution/linear layers reduce bias from heterogeneous distributions.
- Per‑tensor scales for activations are cheaper but require robust dynamic range control (clipping or activation folding).
4. Accumulation and mixed precision
- Accumulate in at least 32 bits for large dot products to avoid overflow and preserve dynamic range; where hardware supports, use 16→32 accumulation with compensated summation.
- Mixed precision policy:
- Weights: 8‑bit per‑channel symmetric quantization.
- Activations: 8‑bit asymmetric per‑tensor with dynamic range calibration.
- Biases and layernorm/softmax internals: 32‑bit float or 16‑bit float depending on sensitivity.
- Final logits and softmax: 32‑bit or 16‑bit to preserve numerical stability.
5. Calibration and clipping
- Calibration pass: run representative data through model to collect min/max or percentile ranges (e.g., 99.9th percentile) for activations.
- Clipping strategies: use percentile clipping or learned clipping parameters (PACT) to reduce outlier impact.
- Zero‑point handling: prefer symmetric quantization for weights; asymmetric for activations when zero offset matters.
6. Training vs post‑training
- Post‑Training Quantization (PTQ): fast, good for many models with calibration; include bias correction and per‑channel weight scaling.
- Quantization‑Aware Training (QAT): emulate quantization during training (fake quant) to recover accuracy for sensitive models; use straight‑through estimator for gradients.
7. Rounding and stochasticity
- Deterministic rounding (nearest, tie to even) for reproducibility.
- Stochastic rounding can help during training to avoid bias but complicates deterministic deployment.
8. Error metrics and validation
- Functional metrics: task accuracy, top‑k, BLEU (NLP), mAP (detection).
- Visual metrics for textures/HDR: PSNR, SSIM, LPIPS, and perceptual thresholds mapped to bit choices.
- Numerical drift checks: layerwise output difference norms; cumulative KL divergence of logits.
- Acceptance criteria: e.g., <0.5% absolute accuracy drop or LPIPS < threshold for visual tasks.
9. Hardware mapping and SIMD considerations
- Pack layout: align tensors to vector lanes; use blocked layouts that match SIMD width (e.g., 16 or 32 lanes).
- Memory layout: planar textures benefit from contiguous channel packing for vector loads; prefer 4‑channel packing for RGBA-like operations.
- Chroma sampling: prefer 4:2:2 over 4:2:0 for ML pipelines where chroma fidelity affects model outputs.
10. Test harness and experiments
- Unit tests: quantize/dequantize roundtrip, accumulation overflow tests, per‑channel vs per‑tensor comparisons.
- Benchmarks: latency, throughput, memory footprint, energy per inference.
- A/B experiments: PTQ vs QAT; symmetric vs asymmetric; accumulation bitwidth 16 vs 32.
---
Quick experimental recipes (ready to run):
1. Layer sensitivity sweep
- For each layer \(L\), quantize only \(L\) to 8‑bit (others remain FP32). Measure task metric drop. Rank layers by sensitivity.
2. Activation clipping ablation
- Compare min/max, 99.9th percentile, and learned clipping (PACT). Plot metric vs clipping percentile.
3. Per‑channel vs per‑tensor
- Compare accuracy and memory overhead; report per‑layer improvement.
RS
*
// Code path for Tensor-Flow & ONNX 32Bit & 8Bit:
// Conceptual conversion down:
load_model(path) -> model_fp32
preprocess(input) -> input_fp32
// Tiered cache & quantize
if (should_compress(input_fp32)) {
compressed = brotli_g_compress(input_fp32)
store_in_cache(compressed)
input_fp32 = brotli_g_decompress(compressed)
}
input_int8 = quantize_to_int8(input_fp32, scale, zero_point)
pack_buffer = pack_8bit_to_u32(input_int8) // 4x8b -> u32 lanes
// Run SIMD/TPU kernel
result_packed = run_simd_dot_product(pack_buffer, model_int8_weights_packed)
// Optional dequantize for final layers
result_fp16 = dequantize_to_fp16(result_packed, scale)
final = run_fp16_refinement(result_fp16, last_layer_fp16)
postprocess(final)
// (c)RS
*
// Testing of the Image Inference Bit Depth 8Bit & 32Bit with results : RS
// Multiple selection paths, With ONNX & TF
// Conversion down : hardware choices (EdgeTPU/Coral, Movidius, Hailo, AVX/Intel/AMD).
#!/usr/bin/env python3
"""
onnx_to_int8_edgetpu_prototype.py
Usage examples at bottom.
"""
import sys, os, time, argparse, glob
from pathlib import Path
# Lightweight optional imports with helpful messages
missing = []
try:
import onnx
except Exception:
onnx = None; missing.append("onnx")
try:
import onnxruntime as ort
except Exception:
ort = None; missing.append("onnxruntime")
try:
from onnxruntime.quantization import quantize_static, CalibrationDataReader, quantize_dynamic
except Exception:
quantize_static = quantize_dynamic = CalibrationDataReader = None; missing.append("onnxruntime.quantization")
try:
from onnx_tf.backend import prepare as onnx_tf_prepare
except Exception:
onnx_tf_prepare = None; missing.append("onnx-tf")
try:
import tensorflow as tf
except Exception:
tf = None; missing.append("tensorflow")
try:
import numpy as np
from PIL import Image
except Exception:
np = None; Image = None; missing.append("numpy/Pillow")
try:
from pycoral.utils.edgetpu import make_interpreter
from pycoral.adapters import common, classify
except Exception:
make_interpreter = None; missing.append("pycoral/tflite-runtime-edgetpu")
def info_missing():
if missing:
print("Optional packages missing:", ", ".join(missing))
print("Install suggestions: pip install onnx onnxruntime onnxruntime-tools onnx-tf tensorflow numpy pillow opencv-python pycoral tflite-runtime")
def load_images_from_dir(d, size, max_images=None):
imgs = []
files = sorted(glob.glob(os.path.join(d, "*.*")))
for f in files[:max_images]:
try:
im = Image.open(f).convert("RGB").resize(size, Image.BILINEAR)
arr = np.asarray(im).astype(np.float32)
imgs.append(arr)
except Exception:
continue
return imgs
def infer_onnx_session(session, inputs, input_name):
out = session.run(None, {input_name: inputs})
return out
def top1_accuracy(preds, labels):
if labels is None: return None
correct = 0
for p, l in zip(preds, labels):
if int(np.argmax(p)) == int(l): correct += 1
return correct / len(labels)
def representative_gen(imgs, input_name):
for im in imgs:
yield {input_name: np.expand_dims(im.astype(np.float32), 0)}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--onnx", required=True)
parser.add_argument("--data_dir", required=True)
parser.add_argument("--labels", default=None)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--num_calib", type=int, default=100)
parser.add_argument("--edgetpu_compile", action="store_true")
parser.add_argument("--device", choices=["cpu","edgetpu"], default="cpu")
args = parser.parse_args()
info_missing()
model_path = args.onnx
if not os.path.exists(model_path):
print("ONNX model not found:", model_path); return
# Load ONNX to inspect input size
if onnx:
m = onnx.load(model_path)
gi = m.graph.input[0].type.tensor_type.shape.dim
try:
h = int(gi[2].dim_value); w = int(gi[3].dim_value)
except Exception:
h,w = 224,224
else:
h,w = 224,224
# Prepare calibration images
imgs = load_images_from_dir(args.data_dir, (w,h), max_images=args.num_calib)
if not imgs:
print("No images found in data_dir"); return
labels = None
if args.labels and os.path.exists(args.labels):
labels = [int(x.strip()) for x in open(args.labels).read().splitlines()]
# ONNX quantization
quant_model = Path("model_int8.onnx")
quant_method = "skipped"
try:
if quantize_static and ort:
class SimpleReader(CalibrationDataReader):
def __init__(self, imgs, name):
self.data = imgs; self.name = name; self.idx = 0
def get_next(self):
if self.idx >= len(self.data): return None
v = {self.name: np.expand_dims(self.data[self.idx].astype(np.float32),0)}
self.idx += 1
return v
sess = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
reader = SimpleReader(imgs, input_name)
quantize_static(model_path, str(quant_model), reader)
quant_method = "static"
elif quantize_dynamic:
quantize_dynamic(model_path, str(quant_model))
quant_method = "dynamic"
except Exception as e:
print("Quantization failed:", e); quant_model = Path(model_path); quant_method = "none"
# ONNX Runtime CPU inference
cpu_results = {}
if ort:
sess = ort.InferenceSession(str(quant_model), providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
warm = 5
for _ in range(warm):
infer_onnx_session(sess, np.expand_dims(imgs[0].astype(np.float32),0), input_name)
times = []
preds = []
for im in imgs:
t0 = time.time()
out = infer_onnx_session(sess, np.expand_dims(im.astype(np.float32),0), input_name)
times.append((time.time()-t0)*1000)
preds.append(out[0][0])
cpu_results = {"latency_ms": sum(times)/len(times), "throughput":1000.0/(sum(times)/len(times)), "top1": top1_accuracy(preds, labels), "quant": quant_method}
# ONNX -> TF -> TFLite INT8
tflite_path = Path("model_int8.tflite")
edgetpu_compiled = False
if onnx_tf_prepare and tf:
try:
tf_rep = onnx_tf_prepare(onnx.load(model_path))
saved = "tmp_saved_model"
tf_rep.export_graph(saved)
converter = tf.lite.TFLiteConverter.from_saved_model(saved)
def rep_gen():
for im in imgs[:args.num_calib]:
yield [np.expand_dims(im.astype(np.float32),0)]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = lambda: (x for x in (np.expand_dims(im.astype(np.float32),0) for im in imgs[:args.num_calib]))
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model = converter.convert()
tflite_path.write_bytes(tflite_model)
except Exception as e:
print("TFLite conversion skipped:", e)
# EdgeTPU compile
if args.edgetpu_compile and tflite_path.exists():
if os.system("which edgetpu_compiler > /dev/null 2>&1") == 0:
print("Running edgetpu_compiler...")
rc = os.system(f"edgetpu_compiler {tflite_path} -o .")
edgetpu_compiled = (rc == 0)
else:
print("edgetpu_compiler not found on PATH; install from Coral site")
# Coral inference
edgetpu_results = {}
if args.device == "edgetpu" and make_interpreter and tflite_path.exists():
try:
compiled = next(Path(".").glob("*.tflite")) # compiled name heuristic
interp = make_interpreter(str(compiled))
interp.allocate_tensors()
input_details = common.input_details(interp)
warm = 5
for _ in range(warm):
common.set_input(interp, np.expand_dims(imgs[0].astype(np.uint8),0))
interp.invoke()
times=[]; preds=[]
for im in imgs:
common.set_input(interp, np.expand_dims(im.astype(np.uint8),0))
t0=time.time(); interp.invoke(); times.append((time.time()-t0)*1000)
out = classify.get_classes(interp, top_k=1)
preds.append(np.eye(1000)[out[0].id] if out else np.zeros(1000))
edgetpu_results = {"latency_ms": sum(times)/len(times), "throughput":1000.0/(sum(times)/len(times)), "top1": top1_accuracy(preds, labels)}
except Exception as e:
print("Coral inference skipped:", e)
print("\nSummary")
print(f"ONNX model: {model_path}")
print(f"Quantized ONNX: {quant_model} method={quant_method}")
if cpu_results:
print(f"CPU latency_ms={cpu_results['latency_ms']:.2f} throughput={cpu_results['throughput']:.2f} top1={cpu_results['top1']}")
if edgetpu_results:
print(f"EdgeTPU latency_ms={edgetpu_results['latency_ms']:.2f} throughput={edgetpu_results['throughput']:.2f} top1={edgetpu_results['top1']}")
print("Done")
if __name__ == "__main__":
main()
// (c)RS
*
Brain Depth:
https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html
https://science.n-helix.com/2022/10/ml.html
https://science.n-helix.com/2026/01/inferencing.html
https://science.n-helix.com/2023/06/tops.html
Training Networks:
https://science.n-helix.com/2023/06/tops.html
https://science.n-helix.com/2023/06/map.html
https://science.n-helix.com/2022/08/jit-dongle.html
https://science.n-helix.com/2022/06/jit-compiler.html
https://science.n-helix.com/2023/02/pm-qos.html
https://science.n-helix.com/2023/06/ptp.html
*****
about:gpu
While we are not supporting 420, Let's Support 422! Rupert S @ Chrome dev
YVU_420: not supported, YUV_420_BIPLANAR: not supported, YUVA_420_TRIPLANAR: not supported
https://science.n-helix.com/2025/07/textureconsume.html
https://science.n-helix.com/2025/07/layertexture.html
https://science.n-helix.com/2025/07/neural.html
https://drive.google.com/file/d/10P7AzvY2RNF3FSPVhkGDgILamsIdoTVM/
code : https://filebin.net/5gz2eswycm9nl963/FRC%20Upscaling%20with%20code%202025.txt
https://filebin.net/5gz2eswycm9nl963/Upscaling%20Colour%20strategy%20-%20With%20Proof%20-%20RS%202025.txt
https://filebin.net/sog7knhxc5tuxbfe/Directory-Sort-RS.zip
