DeepTech & Manufacturing
November 15, 2024
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d-Matrix CEO Sid Sheth Shares His Blueprint for Chip Success | Seed to Silicon

In one of the most exciting podcasts I’ve had the chance to shoot, I sit down with Sid Sheth, the founder of d-Matrix, to explore how a startup can successfully compete against $100B+ incumbents in the semiconductor inference space. Based in the Bay Area, d-Matrix is a leading provider of Digital In-Memory Computing (DIMC) solutions for transformer and generative AI inference acceleration.

The key to mass adoption of AI lies in cost-effective and energy-efficient compute solutions that can scale. Today, most hardware accelerator platforms are optimized for training large language models (LLMs). However, for AI to thrive across diverse use cases, we need large-scale infrastructure designed specifically for inference. Current silicon solutions from incumbents like NVIDIA are suboptimal for this purpose—they repurpose training-focused general-purpose GPUs, add high-bandwidth memory, and market them for inference customers. Yet, inference (expected to become a larger market than training) requires accelerators specifically optimized for individual use cases, such as data centers, edge personal computing, computer vision and others.

In this candid fireside chat, we delve into Sid’s background and how his Gujarati upbringing has instilled a relentless focus on customers, revenue, and profitability. We also discuss the hardware and software decisions that d-Matrix has made to leverage DIMC. While GPUs excel at batch processing for training, inference involves sequential outputs from LLMs, necessitating an architecture optimized for fast matrix multiplication with memory located closer to compute to minimize latency. Sid explains how d-Matrix has optimized for inference without locking itself into a specific architecture and shares insights into how an underdog startup can outpace trillion-dollar giants in the race for inference dominance.

At Z47, we are highly optimistic about opportunities across compute layers, including semiconductors, data centers, and embedded systems. If you’re building in this space and would like to connect, feel free to reach out at semiconductors@z47.com

For more information, write to us: namaste@Z47.com.
Stay connected with Z47.

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d-Matrix CEO Sid Sheth Shares His Blueprint for Chip Success | Seed to Silicon

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In one of the most exciting podcasts I’ve had the chance to shoot, I sit down with Sid Sheth, the founder of d-Matrix, to explore how a startup can successfully compete against $100B+ incumbents in the semiconductor inference space. Based in the Bay Area, d-Matrix is a leading provider of Digital In-Memory Computing (DIMC) solutions for transformer and generative AI inference acceleration.

The key to mass adoption of AI lies in cost-effective and energy-efficient compute solutions that can scale. Today, most hardware accelerator platforms are optimized for training large language models (LLMs). However, for AI to thrive across diverse use cases, we need large-scale infrastructure designed specifically for inference. Current silicon solutions from incumbents like NVIDIA are suboptimal for this purpose—they repurpose training-focused general-purpose GPUs, add high-bandwidth memory, and market them for inference customers. Yet, inference (expected to become a larger market than training) requires accelerators specifically optimized for individual use cases, such as data centers, edge personal computing, computer vision and others.

In this candid fireside chat, we delve into Sid’s background and how his Gujarati upbringing has instilled a relentless focus on customers, revenue, and profitability. We also discuss the hardware and software decisions that d-Matrix has made to leverage DIMC. While GPUs excel at batch processing for training, inference involves sequential outputs from LLMs, necessitating an architecture optimized for fast matrix multiplication with memory located closer to compute to minimize latency. Sid explains how d-Matrix has optimized for inference without locking itself into a specific architecture and shares insights into how an underdog startup can outpace trillion-dollar giants in the race for inference dominance.

At Z47, we are highly optimistic about opportunities across compute layers, including semiconductors, data centers, and embedded systems. If you’re building in this space and would like to connect, feel free to reach out at semiconductors@z47.com

We are excited about the innovation and growth opportunities in this sector.

If you are considering building in the footwear space, we’d love to chat.
Drop us a line at consumer@matrixpartners.in

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Index Performance

+28.1%
Since Jan 2024
NIFTY 500
+19.0%
Since Jan 2024

Z47^fortyseven is up +23.9% since its January 2024 base date, versus Nifty 500's +18.4%, ahead by 550 bps.

The cohort moved +4.7% over the month versus Nifty 500's +2.5%, leading by 220 bps.

Anchored in domestic demand and rising digital adoption, the cohort remained resilient amid global headwinds.

Consumer Tech was the best-performing sector at +9.2% last month, driven by sustained growth in consumer demand and strength in consumer-internet platforms.

Largest Constituents  ·  The Names That Anchor The Index

1.
Eternal
Quick-commerce leadership and continued investment
▲ +12.8%
2.
Groww
Broking market-share gains and margin-funding growth.
▲ +10.4%
3.
Lenskart
Store densification and margin expansion.
▲ +2.4%

Top Gainers  ·  Key Drivers

1 MONTH RETURN
1.
CarTrade
Auto-marketplace dominance and a cash-rich balance sheet.
▲ +59.4%
2.
 Amagi Media Labs
Profitability turnaround and AI-led cloud media adoption.
▲ +31.4%

Top Laggards  ·  Key Drivers

1 MONTH RETURN
1.
Fractal Analytics
Enterprise AI spending trends and post-listing share supply.
▼ -10.8%
2.
MedPlus Health
Pharmacy-margin pressure and competitive intensity.
▼ -6.6%

Key Themes  ·  Latest Results

In Q4FY26, Z47^fortyseven's cohort grew top line ~39% YoY, more than 3x the broad market's ~12% growth.

Operating leverage lifted net margins around 500 bps into positive territory, even as broad-market net margins remained roughly flat.

With 40 of 47 companies now profitable, the cohort reflects a broader shift toward profitable growth over growth at any cost.

AI adoption runs deeper across this cohort than in the broader market, with companies using it to drive growth and reshape demand, not just improve efficiency.

Cash generation is increasingly defining the winners, enabling market leaders like Eternal, CarTrade, and PB Fintech to fund acquisitions and expansion from their own balance sheets.

Market & Macro Context

The cohort saw several block deals this month, including sizeable stake sales in Lenskart, Delhivery, Honasa, and Shadowfax.

Ownership continues to shift from foreign investors to domestic institutions, creating a more durable shareholder base.

AI remained the defining technology investment theme, driving capital deployment across both private and public markets.

IPO Takeaway · Kissht

Listed May 2026

A modest listing pop followed by strong post-listing gains reinforced the market's preference for asset quality and disciplined underwriting over pure loan-book growth.

The listing helped reset perceptions around unsecured lending, creating a constructive valuation anchor for the issuers that follow.

The buyer mix was a notable positive — strong participation from long-only domestic institutions supporting a durable post-listing ownership base.

Net Read

Fundamentals continued to strengthen across the cohort, with growth, margins, and cash generation improving in tandem.

Performance dispersion widened, with profitability and earnings quality increasingly distinguishing the strongest performers from the rest.

Disclaimer

Z47^fortyseven is published for informational purposes only and does not constitute investment advice, or any offer, solicitation, or recommendation to buy or sell securities. Index performance is historical and should not be construed as indicative of future results.

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